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Research Summary – Structure, Examples and Writing Guide

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Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

• Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
• Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
• Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
• Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
• Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
• References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

• Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
• Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
• Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
• Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
• Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
• Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
• Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
• Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
• Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

• Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
• Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
• Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

• Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
• Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
• Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
• Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

• Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
• Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
• Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
• Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
• Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
• Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
• Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
• Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

• Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
• Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
• Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
• Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
• Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

• Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
• Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
• Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
• Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
• Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
• Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

• Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
• Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
• Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
• Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
• Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
• Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Scientific Journal Article Summary Example: Best Practices

We can all agree - condensing complex scientific research into an accurate, engaging summary is tricky.

But with the right approach, you can craft summaries that effectively convey key details and implications to various audiences.

In this post, you'll uncover best practices for summarizing scientific journal articles. You'll learn how to identify core findings, summarize methodologies precisely, convey results properly, and synthesize everything into a cohesive narrative. An example APA-formatted summary is also provided to see these tips in action.

Introduction to Scientific Journal Article Summaries

Summarizing scientific journal articles is an important skill for researchers and students. It allows you to concisely communicate the key objectives, methods, findings, and conclusions of a study to various audiences.

The Art of Condensing Complex Research

When summarizing scientific research, it is essential to identify and highlight the core elements that capture the essence of the study. This involves analyzing complex details and data to extract the most critical information. Key steps include:

Clearly articulating the central research question or objective

Condensing the methods into a simple overview

Highlighting key results and statistics

Summarizing the conclusions and implications

Skills like active reading, critical thinking, and concise writing help distill multidimensional research into accessible summaries.

Target Audience: Tailoring Summaries for Different Readers

Scientific article summaries should be adapted based on the intended reader. For example:

Emphasize key learning points

Define discipline-specific terminology

Focus on practical applications

Academic Peers

Use precise disciplinary language

Include technical details on methodology

Highlight novel contributions to the field

Scientific Journal Article Summary Example for Students

Here is an example summary of a microbiology study tailored specifically for a student reader:

A 2022 study on antimicrobial peptides (AMPs) found that a synthetic AMP named “peptoid-1” effectively killed methicillin-resistant Staphylococcus aureus (MRSA) in lab tests. The peptoid-1 molecule disrupted the bacterial cell membranes of MRSA, including difficult-to-treat biofilms. The research demonstrates the potential of synthetic AMPs as a promising new class of antibiotics to combat drug-resistant superbugs like MRSA. This has important implications for developing urgently needed antibiotics to address the growing global threat of antimicrobial resistance.

This summary briefly explains the key learning points of the study in straightforward language appropriate for students. Technical details are avoided, and emphasis is placed on articulating the essential findings, applications, and implications.

How do you write a summary for a scientific journal article?

A well-written summary of a scientific journal article should cover three main points:

Why the research was done

The first section of your summary should provide background information and context about why the research was conducted. This includes:

The research goals, questions, or hypotheses being investigated

Gaps in existing knowledge the study aims to address

The overall importance of the research topic

For example:

This study investigates the effects of climate change on crop yields in sub-Saharan Africa. Prior research has not examined how higher temperatures may impact staple crops in this region specifically. Understanding climate change effects on agriculture is critical for food security policymaking across developing nations.

What happened in the experiment

The second section should explain the methodology and highlight key findings from the study's experiments, data analysis, or other research activities. Use concise language to describe:

The study sample, materials, and procedures

Statistical analysis techniques

Major results that relate to the research questions

For instance:

Researchers compiled 30 years of temperature data and crop production records from six countries. Using regression analysis, they found higher temperatures significantly reduced wheat and maize yields by an average of 15% and 12%, respectively.

What conclusions the author drew

Finally, summarize the researchers' conclusions, implications, and recommendations based on their results. Mention any limitations noted and future research suggested.

The authors conclude rising temperatures from climate change could seriously impact food security in sub-Saharan Africa. They call for policies to help farmers adapt through heat-tolerant crop varieties and improved irrigation access. Additional research is needed to develop effective adaptation strategies.

Following this basic structure will help you efficiently summarize the essential information in a scientific journal article.

What is journal article summary?

A journal article summary concisely overviews the main points and key takeaways from a scientific paper published in an academic journal. It allows readers to quickly understand the core findings and arguments of the original article without having to read the full text.

An effective summary should:

Identify the main objective or research question the authors aimed to address

Highlight the key methods, data sources, and analytical approaches used

Summarize the major results and main conclusions

Note any limitations or unanswered questions for future research

For example, a summary of a psychology paper might overview the hypothesis tested, experiment methodology, participant demographics, statistical analyses conducted, and whether the findings supported or rejected the original hypothesis.

Summaries are a useful way for scientists to stay current with latest developments across broad fields of research. They also help readers determine if they should invest time reading the full article based on whether the topic and findings are relevant to their own work. As such, summaries should provide enough detail and context to evaluate the scope and implications of the research.

Formatting a Journal Article Summary

When writing a journal article summary, the exact formatting can vary depending on the target publication or audience needs. However, some key elements tend to be consistent:

Citation: Include a full citation of the original paper using the required scholarly style

Background: 1-2 sentences placing the research in context of current knowledge state

Objective: 1 sentence stating the purpose/focus of the study

Methods: 1-2 sentences summarizing the experiment, data, analyses performed

Results: 2-3 sentences describing the major findings

Conclusion: 1-2 sentences covering implications and future directions

The full summary is typically 150-250 words or 8-15 sentences. Brevity and precision are key when condensing a complex study into such a compact overview.

What is the general format for summary of a journal article?

Summarizing a scientific journal article requires capturing the key details while maintaining brevity. Here are some best practices:

Follow the structure of the original paper

Like an abstract, organize your summary by:

Introduction - Cover the background, purpose, and hypothesis.

Methods - Briefly describe the experimental design.

Results - Highlight the main findings without going into excessive detail.

Discussion - Summarize the author's interpretation and conclusions.

Focus on key information

Identify and extract only the most critical details:

Research goals

Sample characteristics

Variables examined

Statistical analyses performed

Major results obtained

Conclusions reached

Maintain objectivity

Present the findings in a neutral tone without inserting your own opinions or judgments.

Use paraphrasing

Summarize points in your own words instead of relying heavily on direct quotes. However, scientifically precise terminology should be retained.

Follow formatting guidelines

Adhere to style formatting per journal or publisher requirements. Most scientific summaries require American Psychological Association (APA) citations.

Keeping summaries clear, accurate, and concise requires practice. But following these research article summary guidelines will help ensure quality. With wisio.app 's tools for discovering papers and translating terminology, scientists can efficiently produce summaries to advance their work.

How do you summarize a journal article in APA?

When summarizing a journal article in APA style, it is important to follow some key guidelines:

Use Your Own Words

Read through the full article and highlight the key points

Write the summary using your own words while staying true to the original meaning

Avoid directly quoting chunks of text from the original

Focus on Relevant Elements

Identify the critical elements like purpose, methods, findings, conclusions

Summarize only details directly relevant to the core focus of the article

Keep contextual details brief or exclude if non-essential

Maintain Clear Distinction

Clearly indicate in the summary which ideas are yours versus the author's

Do not interject your own analysis, evaluation, or interpretation

Keep the summary objective and descriptive in nature

Follow APA Formatting

Include a citation to the original article

Apply proper in-text citations for any verbatim short quotes

Format the summary using standard APA guidelines for font, spacing, etc.

Keep it Brief

Strive to keep the summary less than 10-15% of the original length

Tighten long summaries by removing non-vital details

Aim for brevity while preserving meaning and scientific accuracy

Following these basic tips will help produce an APA-style summary that accurately conveys the essence of the journal article in a clear and concise manner.

Understanding the Structure of Scientific Articles

Delve into the typical structure of scientific journal articles to understand the framework from which summaries are derived.

Dissecting the IMRaD Format

The IMRaD (Introduction, Methods, Results, and Discussion) format is a standard structure used in scientific writing. Understanding this structure is key when summarizing journal articles.

The Introduction presents background context, defines key terms, and states the research objective and hypothesis. When summarizing, capture the main research goals and questions driving the study.

The Methods section provides details on the experimental design, materials, data collection procedures, and statistical analysis. Identify the overall methodology without delving into granular specifics.

The Results present objective findings from the data analysis. Highlight key quantitative outcomes and discoveries in your summary.

The Discussion section interprets the results, explores their significance, compares them to other studies, acknowledges limitations, and suggests future work. Summarize the main conclusions, implications, and next steps discussed.

Decoding Abstracts and Conclusions

Article abstracts concisely overview the purpose, methods, findings, and implications covered in the full text. Leverage abstracts when first assessing articles for relevance.

Conclusions summarize the key points and provide final thoughts. Use them to validate your understanding of the central themes.

Both provide a helpful frame of reference when synthesizing summaries.

Critical Reading for Effective Summarization

Carefully analyze each section and subsection

Annotate and highlight meaningful passages

Identify connections between key ideas

Focus on what findings reveal about the research problem

Capture enough detail to convey original intent

Synthesize using clear, concise language

Thoughtful critical reading builds comprehension essential for quality summarization.

How to Summarize a Research Article

Summarizing a research article requires identifying the core findings and contributions, accurately capturing the methodologies, conveying the key results and implications, and crafting a cohesive narrative. Here is a step-by-step guide:

Identifying Core Findings and Contributions

When summarizing a research article, it is essential to pinpoint the most significant findings and contributions of the study. Key steps include:

Read the abstract and conclusion to understand the major findings.

Highlight unique discoveries, breakthroughs, or advances made.

Note the implications and importance communicated by the authors.

Identify knowledge gaps filled or new frameworks proposed.

Focusing on these elements will help determine the core essence to convey in your summary.

Summarizing Methodologies with Precision

While summarizing the methodologies, avoid oversimplifying complex research processes. Key tips include:

Use concise yet precise language to describe methods applied.

Specify instruments or tools leveraged in the research.

Provide sample sizes and measures captured if relevant.

Note statistical or analytical techniques utilized.

Maintaining key methodological details demonstrates analytical rigor when sharing the research with others.

Conveying Results and Their Implications

An effective summary should clearly communicate the study's results and why they matter. To accomplish this:

Report quantitative findings or qualitative discoveries made.

Contextualize results using benchmarks, comparisons, or real-world impacts.

Connect results back to the research aims and knowledge gaps identified.

Discuss limitations along with future research needed.

This enables readers to grasp the meaningfulness of the results.

Crafting a Cohesive Narrative

Finally, structure the various summary elements into a cohesive overview:

Organize content using section headers around aims, methods, results, and conclusions.

Use transition words (e.g. “additionally,” “in contrast,” “as a result”) to improve flow.

Focus on information that supports the core findings and contributions of the work.

Avoid excessive details and maintain brevity.

Following these steps will produce a concise yet insightful summary showcasing the relevance of the research.

Scientific Journal Article Summary Example APA Format

Adhering to proper formatting guidelines is critical when summarizing scientific journal articles, especially for academic purposes. The American Psychological Association (APA) style provides clear standards that enable precise, uniform communication across scientific disciplines.

Adhering to APA Style in Summaries

Following APA style lends credibility and ensures readers can easily reference sources. Key elements include:

Properly formatting in-text citations and references

Using headings and subheadings to organize content

Applying title case capitalization

Using active voice and clear language

Formatting title page with running head, page numbers, and other elements

Adhering to these conventions helps establish summaries as reputable academic works worthy of consideration.

Example of an APA-Formatted Summary

Here is an example of a properly formatted APA summary:

Smith, J. (2021). The impact of climate change on coral reef ecosystems. Marine Biology , 166 (3), 201–215. https://doi.org/10.1007/s00227-021-03876-8

This study examined the effects of rising ocean temperatures and acidification on coral reef health over 5 years. The author tracked changes in coral cover and biodiversity across 12 reef sites in the Caribbean Sea. On average, coral cover declined by 18.7% and species richness decreased by 22.4% on reefs exposed to prolonged marine heatwaves. The declines were attributed to mass coral bleaching triggered by unusually warm water temperatures. The findings suggest climate change may severely degrade coral reef ecosystems within decades. Further research into mitigation strategies is warranted to preserve these valuable marine habitats.

Key elements like the citation, use of third-person perspective, headings, and formal academic language adhere to APA conventions.

Common Mistakes to Avoid in APA Summaries

When writing APA-style summaries, writers should avoid:

Neglecting to include a full citation for the original work

Using first-person pronouns like “I” or “we”

Inserting opinions or commentary from the summarizer

Failing to use headings to organize content

Including direct quotes from the original text

Avoiding these pitfalls will ensure an APA-compliant summary format.

Practical Tips for Writing Scientific Summaries

Language and terminology: clarity above all.

When summarizing scientific research, it is crucial to use clear, precise language and terminology. Avoid vague or ambiguous phrasing, and opt for specificity whenever possible. Define key terms, acronyms, or concepts that may be unfamiliar to readers. Simplify complex statistical analysis or scientific jargon for general audiences without losing integrity. Stick to plain language with straightforward syntax to ensure readers grasp the key findings.

Brevity vs. Completeness: Striking the Right Balance

Balancing brevity and completeness presents a challenge when summarizing scientific papers. Focus on highlighting the central objective, methodology, results, and conclusions. Resist dwelling on intricate experimental details or tangential discussions. However, take care not to oversimplify complex research. Seek to distill the essence without omitting information that substantively impacts the interpretation or reproducibility of the study. Adhere to word limits when required but avoid excluding key facts, figures, or takeaways in the quest for brevity.

Ethical Considerations in Summarizing Research

When writing scientific summaries, it is vital to represent the original piece fairly and avoid misconstruing the author's intent. Exercise caution when paraphrasing specialized statistical analysis or scientific terminology. Cite sources properly, and refrain from plagiarizing significant portions of the original text. Also, recognize the limitations of summarization; for complete details, readers should consult the primary literature. By maintaining high ethical standards, scientific summarizers uphold the integrity of research communication.

Conclusion: Synthesizing the Essentials

Summarizing scientific journal articles effectively requires adhering to several key best practices. By focusing on the article's key findings, methodology, and conclusions, skilled summarizers can efficiently communicate the essential information to readers.

Recapitulating Best Practices for Summary Writing

When summarizing a scientific article, it's important to:

Highlight the important methods, data, and analyses used in the study

Note the study's core findings and conclusions

Maintain the authors' original meaning and intent

Follow applicable formatting guidelines (e.g. APA style)

Adhering to these principles helps preserve the accuracy and integrity of the research while making the information more readily digestible.

Summary of a Research Article Example

Here is an example summary incorporating the best practices covered in this article:

Smith et al. (2021) set out to understand the effects of climate change on crop yields. The authors analyzed 30 years of temperature, rainfall, and corn production data across major farming regions of the U.S. Midwest. They found that increased temperatures and shifting rainfall patterns have already caused measurable declines in corn yields over the past decade. Based on predictive climate models, the authors expect these negative impacts on crop productivity to accelerate in the coming years if mitigation measures are not adopted. This clearly structured summary concisely conveys the objective, methods, key results, and conclusions of the article while maintaining authorial intent and voice. The formatting adheres to APA guidelines.

In this way, skillful summarization enables efficient scientific communication while upholding standards of accuracy and integrity.

Antonio Carlos Filho @acfilho_dev

• How it works

Writing a Summary – Explanation & Examples

Published by Alvin Nicolas at October 17th, 2023 , Revised On October 17, 2023

In a world bombarded with vast amounts of information, condensing and presenting data in a digestible format becomes invaluable. Enter summaries. 

A summary is a brief and concise account of the main points of a larger body of work. It distils complex ideas, narratives, or data into a version that is quicker to read and easier to understand yet still retains the essence of the original content.

Importance of Summaries

The importance of summarising extends far beyond just making reading more manageable. In academic settings, summaries aid students in understanding and retaining complex materials, from textbook chapters to research articles. They also serve as tools to showcase one’s grasp of the subject in essays and reports. 

In professional arenas, summaries are pivotal in business reports, executive briefings, and even emails where key points need to be conveyed quickly to decision-makers. Meanwhile, summarising skills come into play in our personal lives when we relay news stories to friends, recap a movie plot, or even scroll through condensed news or app notifications on our smartphones.

Why Do We Write Summaries?

In our modern information age, the sheer volume of content available can be overwhelming. From detailed research papers to comprehensive news articles, the quest for knowledge is often met with lengthy and complex resources. This is where the power of a well-crafted summary comes into play. But what drives us to create or seek out summaries? Let’s discuss.

Makes Important Things Easy to Remember

At the heart of summarisation is the goal to understand. A well-written summary aids in digesting complex material. By distilling larger works into their core points, we reinforce the primary messages, making them easier to remember. This is especially crucial for students who need to retain knowledge for exams or professionals prepping for a meeting based on a lengthy report.

Simplification of Complex Topics

Not everyone is an expert in every field. Often, topics come laden with jargon, intricate details, and nuanced arguments. Summaries act as a bridge, translating this complexity into accessible and straightforward content. This is especially beneficial for individuals new to a topic or those who need just the highlights without the intricacies.

Aid in Researching and Understanding Diverse Sources

Researchers, writers, and academics often wade through many sources when working on a project. This involves finding sources of different types, such as primary or secondary sources , and then understanding their content. Sifting through each source in its entirety can be time-consuming. Summaries offer a streamlined way to understand each source’s main arguments or findings, making synthesising information from diverse materials more efficient.

Condensing Information for Presentation or Sharing

In professional settings, there is often a need to present findings, updates, or recommendations to stakeholders. An executive might not have the time to go through a 50-page report, but they would certainly appreciate a concise summary highlighting the key points. Similarly, in our personal lives, we often summarise movie plots, book stories, or news events when sharing with friends or family.

Characteristics of a Good Summary

Crafting an effective summary is an art. It’s more than just shortening a piece of content; it is about capturing the essence of the original work in a manner that is both accessible and true to its intent. Let’s explore the primary characteristics that distinguish a good summary from a mediocre one:

Conciseness

At the core of a summary is the concept of brevity. But being concise doesn’t mean leaving out vital information. A good summary will:

• Eliminate superfluous details or repetitive points.
• Focus on the primary arguments, events, or findings.
• Use succinct language without compromising the message.

Objectivity

Summarising is not about infusing personal opinions or interpretations. A quality summary will:

• Stick to the facts as presented in the original content.
• Avoid introducing personal biases or perspectives.
• Represent the original author’s intent faithfully.

A summary is meant to simplify and make content accessible. This is only possible if the summary itself is easy to understand. Ensuring clarity involves:

• Avoiding jargon or technical terms unless they are essential to the content. If they are used, they should be clearly defined.
• Structuring sentences in a straightforward manner.
• Making sure ideas are presented in a way that even someone unfamiliar with the topic can grasp the primary points.

A jumble of ideas, no matter how concise, will not make for a good summary. Coherence ensures that there’s a logical flow to the summarised content. A coherent summary will:

• Maintain a logical sequence, often following the structure of the original content.
• Use transition words or phrases to connect ideas and ensure smooth progression.
• Group related ideas together to provide structure and avoid confusion.

Steps of Writing a Summary

The process of creating a compelling summary is not merely about cutting down content. It involves understanding, discerning, and crafting. Here is a step-by-step guide to writing a summary that encapsulates the essence of the original work:

Reading Actively

Engage deeply with the content to ensure a thorough understanding.

• Read the entire document or work first to grasp its overall intent and structure.
• On the second read, underline or highlight the standout points or pivotal moments.
• Make brief notes in the margins or on a separate sheet, capturing the core ideas in your own words.

Identifying the Main Idea

Determine the backbone of the content, around which all other details revolve.

• Ask yourself: “What is the primary message or theme the author wants to convey?”
• This can often be found in the title, introduction, or conclusion of a piece.
• Frame the main idea in a clear and concise statement to guide your summary.

List Key Supporting Points

Understand the pillars that uphold the main idea, providing evidence or depth to the primary message.

• Refer back to the points you underlined or highlighted during your active reading.
• Note major arguments, evidence, or examples that the author uses to back up the main idea.
• Prioritise these points based on their significance to the main idea.

Draft the Summary

Convert your understanding into a condensed, coherent version of the original.

• Start with a statement of the main idea.
• Follow with the key supporting points, maintaining logical order.
• Avoid including trivial details or examples unless they’re crucial to the primary message.
• Use your own words, ensuring you are not plagiarising the original content.

Fine-tune your draft to ensure clarity, accuracy, and brevity.

• Read your draft aloud to check for flow and coherence.
• Ensure that your summary remains objective, avoiding any personal interpretations or biases.
• Check the length. See if any non-essential details can be removed without sacrificing understanding if it is too lengthy.
• Ensure clarity by ensuring the language is straightforward, and the main ideas are easily grasped.

The research done by our experts have:

• Precision and Clarity
• Zero Plagiarism
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Dos and Don’ts of Summarising Key Points

Summarising, while seemingly straightforward, comes with its nuances. Properly condensing content demands a balance between brevity and fidelity to the original work. To aid in crafting exemplary summaries, here is a guide on the essential dos and don’ts:

Use your Own Words

This ensures that you have truly understood the content and are not merely parroting it. It also prevents issues of plagiarism.

Tip: After reading the original content, take a moment to reflect on it. Then, without looking at the source, write down the main points in your own words.

Attribute Sources Properly

Giving credit is both ethical and provides context to readers, helping them trace back to the original work if needed. How to cite sources correctly is a skill every writer should master.

Tip: Use signal phrases like “According to [Author/Source]…” or “As [Author/Source] points out…” to seamlessly incorporate attributions.

Ensure Accuracy of the Summarised Content

A summary should be a reliable reflection of the original content. Distorting or misrepresenting the original ideas compromises the integrity of the summary.

Tip: After drafting your summary, cross-check with the original content to ensure all key points are represented accurately and ensure you are referencing credible sources .

Avoid Copy-Pasting Chunks of Original Content

This not only raises plagiarism concerns but also shows a lack of genuine engagement with the material.

Tip: If a particular phrase or sentence from the original is pivotal and cannot be reworded without losing its essence, use block quotes , quotation marks, and attribute the source.

Do not Inject your Personal Opinion

A summary should be an objective reflection of the source material. Introducing personal biases or interpretations can mislead readers.

Tip: Stick to the facts and arguments presented in the original content. If you find yourself writing “I think” or “In my opinion,” reevaluate the sentence.

Do not Omit Crucial Information

While a summary is meant to be concise, it shouldn’t be at the expense of vital details that are essential to understanding the original content’s core message.

Tip: Prioritise information. Always include the main idea and its primary supports. If you are unsure whether a detail is crucial, consider its impact on the overall message.

Examples of Summaries

Here are a few examples that will help you get a clearer view of how to write a summary. 

Example 1: Summary of a News Article

Original Article: The article reports on the recent discovery of a rare species of frog in the Amazon rainforest. The frog, named the “Emerald Whisperer” due to its unique green hue and the soft chirping sounds it makes, was found by a team of researchers from the University of Texas. The discovery is significant as it offers insights into the biodiversity of the region, and the Emerald Whisperer might also play a pivotal role in understanding the ecosystem balance.

Summary: Researchers from the University of Texas have discovered a unique frog, termed the “Emerald Whisperer,” in the Amazon rainforest. This finding sheds light on the region’s biodiversity and underscores the importance of the frog in ecological studies.

Example 2: Summary of a Research Paper

Original Paper: In a study titled “The Impact of Urbanisation on Bee Populations,” researchers conducted a year-long observation on bee colonies in three urban areas and three rural areas. Using specific metrics like colony health, bee productivity, and population size, the study found that urban environments saw a 30% decline in bee populations compared to rural settings. The research attributes this decline to factors like pollution, reduced green spaces, and increased temperatures in urban areas.

Summary: A study analysing the effects of urbanisation on bee colonies found a significant 30% decrease in bee populations in urban settings compared to rural areas. The decline is linked to urban factors such as pollution, diminished greenery, and elevated temperatures.

Example 3: Summary of a Novel

Original Story: In the novel “Winds of Fate,” protagonist Clara is trapped in a timeless city where memories dictate reality. Throughout her journey, she encounters characters from her past, present, and imagined future. Battling her own perceptions and a menacing shadow figure, Clara seeks an elusive gateway to return to her real world. In the climax, she confronts the shadow, which turns out to be her own fear, and upon overcoming it, she finds her way back, realising that reality is subjective.

Summary: “Winds of Fate” follows Clara’s adventures in a surreal city shaped by memories. Confronting figures from various phases of her life and battling a symbolic shadow of her own fear, Clara eventually discovers that reality’s perception is malleable and subjective.

Frequently Asked Questions

How long is a summary.

A summary condenses a larger piece of content, capturing its main points and essence.  It is usually one-fourth of the original content.

What is a summary?

A summary is a concise representation of a larger text or content, highlighting its main ideas and points. It distils complex information into a shorter form, allowing readers to quickly grasp the essence of the original material without delving into extensive details. Summaries prioritise clarity, brevity, and accuracy.

When should I write a summary?

Write a summary when you need to condense lengthy content for easier comprehension and recall. It’s useful in academic settings, professional reports, presentations, and research to highlight key points. Summaries aid in comparing multiple sources, preparing for discussions, and sharing essential details of extensive materials efficiently with others.

How can I summarise a source without plagiarising?

To summarise without plagiarising: Read the source thoroughly, understand its main ideas, and then write the summary in your own words. Avoid copying phrases verbatim. Attribute the source properly. Use paraphrasing techniques and cross-check your summary against the original to ensure distinctiveness while retaining accuracy. Always prioritise understanding over direct replication.

What is the difference between a summary and an abstract?

A summary condenses a text, capturing its main points from various content types like books, articles, or movies. An abstract, typically found in research papers and scientific articles, provides a brief overview of the study’s purpose, methodology, results, and conclusions. Both offer concise versions, but abstracts are more structured and specific.

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The CRAAP Test is an acronym used as a checklist to help individuals evaluate the credibility and relevance of sources, especially in academic or research contexts. CRAAP stands for Currency, Relevance, Authority, Accuracy, and Purpose. Each of these criteria can help a researcher determine if a source is trustworthy and suitable for their needs.

In academia, research, journalism, and writing, the skill of quoting sources is fundamental. Accurate and proper quoting adds credibility to your work and demonstrates respect for the original authors and their ideas.

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• How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.

Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

• Read the text
• Break it down into sections
• Identify the key points in each section
• Write the summary
• Check the summary against the article

Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

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Table of contents

When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.

There are many situations in which you might have to summarise an article or other source:

• As a stand-alone assignment to show you’ve understood the material
• To keep notes that will help you remember what you’ve read
• To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.

In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

• Scan the article quickly to get a sense of its topic and overall shape.
• Read the article carefully, highlighting important points and taking notes as you read.
• Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

• Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
• Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
• Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

• You’ve accurately represented the author’s work
• You haven’t missed any essential information
• The phrasing is not too similar to any sentences in the original.

If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

A summary is a short overview of the main points of an article or other source, written entirely in your own words.

Save yourself some time with the free summariser.

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.

With the summariser tool you can easily adjust the length of your summary.

You might have to write a summary of a source:

• As a stand-alone assignment to prove you understand the material
• For your own use, to keep notes on your reading
• To provide an overview of other researchers’ work in a literature review
• In a paper , to summarise or introduce a relevant study

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/

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Not every source you found should be included in your annotated bibliography or lit review. Only include the most relevant and most important sources.

Get Organized

• Lit Review Prep Use this template to help you evaluate your sources, create article summaries for an annotated bibliography, and a synthesis matrix for your lit review outline.

Summarize your Sources

Summarize each source: Determine the most important and relevant information from each source, such as the findings, methodology, theories, etc.  Consider using an article summary, or study summary to help you organize and summarize your sources.

Paraphrasing

• Use your own words, and do not copy and paste the abstract
• The library's tutorials about plagiarism are excellent, and will help you with paraphasing correctly

Annotated Bibliographies

     Annotated bibliographies can help you clearly see and understand the research before diving into organizing and writing your literature review.        Although typically part of the "summarize" step of the literature review, annotations should not merely be summaries of each article - instead, they should be critical evaluations of the source, and help determine a source's usefulness for your lit review.  

Definition:

A list of citations on a particular topic followed by an evaluation of the source’s argument and other relevant material including its intended audience, sources of evidence, and methodology

• Explore your topic.
• Appraise issues or factors associated with your professional practice and research topic.
• Help you get started with the literature review.
• Think critically about your topic, and the literature.

Steps to Creating an Annotated Bibliography:

• Find Your Sources
• Read Your Sources
• Identify the Most Relevant Sources
• Cite your Sources
• Write Annotations

Annotated Bibliography Resources

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How to Summarize a Journal Article

Last Updated: February 21, 2024 Approved

Reading Article

Planning draft, writing summary, sample summaries.

This article was co-authored by Richard Perkins . Richard Perkins is a Writing Coach, Academic English Coordinator, and the Founder of PLC Learning Center. With over 24 years of education experience, he gives teachers tools to teach writing to students and works with elementary to university level students to become proficient, confident writers. Richard is a fellow at the National Writing Project. As a teacher leader and consultant at California State University Long Beach's Global Education Project, Mr. Perkins creates and presents teacher workshops that integrate the U.N.'s 17 Sustainable Development Goals in the K-12 curriculum. He holds a BA in Communications and TV from The University of Southern California and an MEd from California State University Dominguez Hills. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article has 24 testimonials from our readers, earning it our reader-approved status. This article has been viewed 1,414,130 times.

Summarizing a journal article is presenting a focused overview of a research study published in a peer-reviewed, scholarly source. A journal article summary provides readers with a short descriptive commentary, giving them some insight into the article's focus. Writing and summarizing a journal article is a common task for college students and research assistants alike. With a little practice, you can learn to read the article effectively with an eye for summary, plan a successful summary, and write it to completion.

• The purpose of an abstract is to allow researchers to quickly scan a journal and see if specific research articles are applicable to the work they are doing. If you're collecting research on immune system responses in rodents, you'll be able to know in 100 words not only whether or not the research is in your field, but whether the conclusions back up your own findings, or differ from it.
• Remember that an abstract and an article summary are two different things, so an article summary that looks just like the abstract is a poor summary. [1] X Research source An abstract is highly condensed and cannot provide the same level of detail regarding the research and its conclusions that a summary can.

• You still need to go back and actually read the article after coming to the conclusion, but only if the research is still applicable. If you're collecting research, you may not need to digest another source that backs up your own if you're looking for some dissenting opinions.

• Look for words like hypothesis, results, typically, generally, or clearly to give you hints about which sentence is the thesis.
• Underline, highlight, or rewrite the main argument of the research in the margins. Keep yourself focused on this main point, so you'll be able to connect the rest of the article back to that idea and see how it works together.
• In the humanities, it's sometimes more difficult to get a clear and concise thesis for an article because they are often about complex, abstract ideas (like class in post-modern poetics, or feminist film, for example). If it's unclear, try to articulate it for yourself, as best as you can understand the author's ideas and what they're attempting to prove with their analysis.
• Try to analyze the author's tone, looking at some of the keywords that really tells you what they are trying to get across to you.

• Different areas of focus within a journal article will usually be marked with subsection titles that target a specific step or development during the course of the research study. The titles for these sub-sections are usually bold and in a larger font than the remaining text.
• Keep in mind that academic journals are often dry reading. Is it absolutely necessary to read through the author's 500 word proof of the formulas used in the glycerine solution fed to the frogs in the research study? Maybe, but probably not. It's usually not essential to read research articles word-for-word, as long as you're picking out the main idea, and why the content is there in the first place.

• These segments will usually include an introduction, methodology, research results, and a conclusion in addition to a listing of references.

• When you're first getting started, it's helpful to turn your filter off and just quickly write out what you remember from the article. These will help you discover the main points necessary to summarize.

• Depending on the research, you may want to describe the theoretical background of the research, or the assumptions of the researchers. In scientific writing, it's important to clearly summarize the hypotheses the researchers outlined before undertaking the research, as well as the procedures used in following through with the project. Summarize briefly any statistical results and include a rudimentary interpretation of the data for your summary.
• In humanities articles, it's usually good to summarize the fundamental assumptions and the school of thought from which the author comes, as well as the examples and the ideas presented throughout the article.

• Any words or terms that the author coins need to be included and discussed in your summary.

• As a general rule of thumb, you can probably make one paragraph per main point, ending up with no more than 500-1000 words, for most academic articles. For most journal summaries, you'll be writing several short paragraphs that summarize each separate portion of the journal article.

• In scientific articles, usually there is an introduction which establishes the background for the experiment or study, and won't provide you with much to summarize. It will be followed by the development of a research question and testing procedures, though, which are key in dictating the content for the rest of the article.

• The specifics of the testing procedures don't usually need to be included in your summary in their entirety; they should be reduced to a simple idea of how the research question was addressed. The results of the study will usually be processed data, sometimes accompanied by raw, pre-process data. Only the processed data needs to be included in the summary.

• Make sure your summary covers the research question, the conclusions/results, and how those results were achieved. These are crucial parts of the article and cannot be left out.

• This is sometimes more important in summaries dealing with articles in the humanities. For example, it might be helpful to unpack dense arguments about poet George Herbert's relationship to the divine with more pedestrian summaries: "The author seeks to humanize Herbert by discussing his daily routines, as opposed to his philosophies."

• This can be difficult for some inexperienced research writers to get the hang of at first, but remember to keep the "I" out of it.

• Check verbs after writing. If you're using the same ones over and over, your reader will get bored. In this case, try to go back and really see if you can make really efficient choices.

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• ↑ https://owl.english.purdue.edu/owl/owlprint/930/
• ↑ https://student.unsw.edu.au/writing-critical-review
• ↑ http://web.pdx.edu/~jduh/courses/faq/JouranlArticleSearch.htm
• ↑ http://web.cortland.edu/hendrick/journalarticle.pdf

About This Article

To summarize a journal article, start by reading the author's abstract, which tells you the main argument of the article. Next, read the article carefully, highlighting portions, identifying key vocabulary, and taking notes as you go. In your summary, define the research question, indicate the methodology used, and focus mostly on the results of the research. Use your notes to help you stay focused on the main argument and always keep your tone objective—avoid using personal pronouns and drawing your own conclusions. For tips on how to read through the journal article thoroughly, such as starting with the conclusion, keep reading! Did this summary help you? Yes No

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A critique asks you to evaluate an article and the author’s argument. You will need to look critically at what the author is claiming, evaluate the research methods, and look for possible problems with, or applications of, the researcher’s claims.

Introduction

Give an overview of the author’s main points and how the author supports those points. Explain what the author found and describe the process they used to arrive at this conclusion.

Body Paragraphs

Interpret the information from the article:

• Does the author review previous studies? Is current and relevant research used?
• What type of research was used – empirical studies, anecdotal material, or personal observations?
• Was the sample too small to generalize from?
• Was the participant group lacking in diversity (race, gender, age, education, socioeconomic status, etc.)
• For instance, volunteers gathered at a health food store might have different attitudes about nutrition than the population at large.
• How useful does this work seem to you? How does the author suggest the findings could be applied and how do you believe they could be applied?
• How could the study have been improved in your opinion?
• Does the author appear to have any biases (related to gender, race, class, or politics)?
• Is the writing clear and easy to follow? Does the author’s tone add to or detract from the article?
• How useful are the visuals (such as tables, charts, maps, photographs) included, if any? How do they help to illustrate the argument? Are they confusing or hard to read?
• What further research might be conducted on this subject?

Try to synthesize the pieces of your critique to emphasize your own main points about the author’s work, relating the researcher’s work to your own knowledge or to topics being discussed in your course.

From the Center for Academic Excellence (opens in a new window), University of Saint Joseph Connecticut

Additional Resources

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Writing an Article Critique (from The University of Arizona Global Campus Writing Center)

How to Critique an Article (from Essaypro.com)

How to Write an Article Critique (from EliteEditing.com.au)

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BIO 101 - Finding, Reading, Summarizing, Critiquing, and Citing Scientific Research Articles

• Finding Science Research Articles
• What's Peer Reviewed?

Resources for Summarizing

Resources for critiquing.

A Summary is a shortened version of the main points of the article, in your own words. A summary is factual, not an opinion or interpretation.

• How to Summarize a Research Article from UConn (PDF) The process of writing a summary of an article
• Summarizing, Paraphrasing, Quoting from the Harvard Guide to Using Sources Examples of the difference between summarizing, paraphrasing, and quoting
• How to summarize a research article (PDF) Handout from University of the Frasier Valley writing center

A Critique (or Review) is a judgment about how good the article is according to some standard criteria. It can include interpretation and comparison but it's not a reflection or emotional reaction to the article.

• How to critique a journal article from the University of Illinois at Springfield (PDF) Questions to answer in a critique
• How to Read and Review a Scientific Article from Duke University (PDF) First summarize, then critique
• Checklist for critiquing an article (Word file) A list of things to cover in a critique
• A Rough Guide to Bad Science Printable infographic style guide to bad science and bad science writing
• Rough Guide to Types of Scientific Evidence A printable infographic style guide to types of science studies and their relative strength
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Research Summary: Social Determinants of Health

• Social determinants of health are the non-medical factors that influence health outcomes.
• This pages provides an overview of CDC research on social determinants of health.
• This searchable list of peer-reviewed articles is categorized according to the Healthy People 2030 framework.

What CDC is doing

CDC conducts and publishes research on the social determinants of health (SDOH). This page offers a searchable list of recent peer-reviewed articles written by CDC researchers on various SDOH topics. These are organized according to the Healthy People 2030 place-based framework:

• Economic stability explores the link between people's financial resources (like income, cost of living, and socioeconomic status) and their health. Key issues include poverty, employment, food security, and housing stability.
• Education examines the relationship between education and health and well-being. Important areas include graduation rates, higher education, educational attainment, language and literacy, and early childhood education and development.
• Social and community context focuses on how the characteristics of environments where people live, learn, work, and play affect their health and well-being. It covers topics like community cohesion, civic participation, discrimination, racism, xenophobia, cultural norms, interpersonal violence, workplace conditions, and incarceration. Another contextual factor is immigration status, especially for people who migrated from less-developed countries.
• Health and healthcare looks at how people's access to and understanding of health services impact their health. Issues include healthcare access, health insurance coverage, English language proficiency, health literacy, and the health implications for people who migrate to or within the United States, even temporarily.
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Features in extractive supervised single-document summarization: case of Persian news

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• Published: 08 May 2024

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• Hosein Rezaei 1 ,
• Seyed Amid Moeinzadeh Mirhosseini 1 ,
• Azar Shahgholian 2 &
• Mohamad Saraee   ORCID: orcid.org/0000-0002-3283-1912 3  

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Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In extractive supervised single-document approaches, the system will not generate sentences. Instead, via supervised learning, it learns how to score sentences within the document based on some textual features and subsequently selects those with the highest rank. Therefore, the core objective is ranking, which enormously depends on the document structure and context. These dependencies have been unnoticed by many state-of-the-art solutions. In this work, document-related features such as topic and relative length are integrated into the vectors of every sentence to enhance the quality of summaries. Our experiment results show that the system takes contextual and structural patterns into account, which will increase the precision of the learned model. Consequently, our method will produce more comprehensive and concise summaries.

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1 Introduction

From the early days of artificial intelligence, automatically summarizing a text has been an interesting task for many researchers. Followed by the advance of the World Wide Web and the advent of concepts such as social networks, Big Data, and cloud computing, among others, text summarization has become a crucial task in many applications (Maña-López et al., 2004 ; Mishra, et al., 2014 ; Sakai & Sparck-Jones, 2001 ). For example, in many search engines and document retrieval systems, it is essential to display a portion of each result entry that is representative of the whole text (Roussinov & Chen, 2001 ; Turpin et al., 2007 ). It is also becoming essential for managers and the general public to get the gist of news and articles immediately in order to save time while being inundated with information on all social media (Mckeown et al., 2005 ).

Researchers have approached this challenge from various perspectives and have obtained some good results (Barrera & Verma, 2012 ; Ferreira, et al., 2014 ). However, this area continues to present more research challenges and has a long path to maturity.

One method of investigating this challenge is supervised extractive summarization, which compared to unsupervised methods, is trained using labelled data. Extractive implementations use a ranking mechanism and select top-n-ranked sentences as the summary (Gupta & Lehal, 2010 ). Sentences of a document are represented as vectors of features. A rank will be assigned to each sentence using summarization corpora, based on their presence in golden summaries (which contain sentences of the original documents, normally selected by human). The system should then learn how to use those features to predict the rank of sentences in any given text. Various machine learning approaches such as regression and classification algorithms are used to perform the ranking task (Hirao et al., 2002 ; Wong et al., 2008 ).

As far as we know, in all current implementations, sets of sentence vectors of every document are merged to compose a larger set, which is then passed to the learning model as a matrix. In this approach, the locality of ranks is disregarded. In other words, the rank of sentences is highly relative to the context and document. A sentence might be ranked high in one document while being ranked lower in another. As a result, merging sentences of a whole dataset into a matrix removes document boundaries, and a main source of information will be lost.

We addressed this issue by taking certain features of documents into account, such as their length, topical category, and so on, and some new sentence features that also reflect document properties. Thus, although document boundaries are not explicitly reconstituted, more information will be provided to the model, and ranking could be done with respect to the local features of the document. Our experiments show that this rectification improves both the performance of the learned model and the quality of produced summaries.

We also represent a new baseline for evaluating extractive text summarizers, which can be used to measure the performance of any summarizing method more accurately.

We examined our hypothesis on a low resource language, namely Persian, which has several properties making it an appropriate case for our study. For example, a large proportion of Persian verbs are light verb constructions (Samvelian & Faghiri, 2013 ). In addition, due to its writing system, Persian faces relatively more homonyms, words with different meanings with the same written forms (Shamsfard, 2011 ). Again, context is used to determine the intended meaning.

Furthermore, in Persian, some phrases or words can be omitted if their symmetry is present in the context (Shamsfard, 2011 ). All of these and many other properties create challenges for summarization. As our approach is to bring the context of the document into consideration, Persian is a good candidate for our case study. However, the primary insight of this study is broadly relevant and not language-specific.

The remainder of this paper is organized as follows. Section  2 reviews related works. Section  3 presents the proposed method and evaluation measures. Section  4 discusses how the experiments are set up. The results are discussed in Sect.  5 , and finally, Sect.  6 concludes the paper.

2 Related works

Both academic and industrial disciplines have widely studied text summarization. Text summarization methods may be classified into different types. Based on the input type, there are single-document (Patil et al., 2015 ; Torres-Moreno, 2014 ) vs. multi-document summarization methods (Christensen et al., 2013 ; Erkan & Radev, 2004 ; Nenkova et al., 2006 ), in the latter, multiple documents about a topic are summarized. Considering the language mixture, there are monolingual, bilingual, and multi-lingual methods (Gambhir & Gupta, 2017 ). There are also “query-focused” methods in which a summary relevant to a given query is produced (Varadarajan & Hristidis, 2006 ). However, from the perspective of summary generation procedure, there are two main approaches: abstractive vs. extractive (Hahn & Mani, 2000 ).

Abstractive approaches try to generate a new short text based on the concepts understood from the original text (Moratanch & Chitrakala, 2016 ). These approaches usually require a complete pass through an NLP pipeline and are faced with many complexities and challenges (Lloret & Palomar, 2012 ). The abstractive approach relies on linguistic methods to examine and interpret the text to find new concepts and expressions. The output is a new shorter text which consists of the essential information from the original text document (Gupta & Lehal, 2010 ).

On the other hand, extractive approaches select a few sentences from the document based on some measures to place them in the generated summary (Gupta & Lehal, 2010 ). A broad range of methods has been examined in this sector, including graph-based (Gupta & Lehal, 2010 ; Mihalcea & Tarau, 2004 ), unsupervised (Mihalcea & Tarau, 2004 ; Rautray & Balabantaray, 2017 ) and supervised (corpus-based) learning (Shafiee & Shamsfard, 2018 ; Silva, et al., 2015 ; Wong et al., 2008 ). In supervised methods, training data is generally needed to select important content from the documents. In these methods, the problem is usually reduced to a classification or regression problem, and machine learning techniques are applied to the dataset of documents and their gold summaries represented by some features. Support vector machines (SVM) (Ouyang et al., 2011 ) and neural networks (Fattah, 2014 ) are more popular sentence classification algorithms.

The key step in extractive summarization is to determine the importance of sentences in the document (Fang et al., 2017 ). Previous studies examine the ordinal position of sentences (Edmundson, 1969 ), (Fattah & Ren, 2008 ), length of sentences (Wong et al., 2008 ), the ratio of nouns, the ratio of verbs, ratio of adjectives, ratio of adverbs (Dlikman, 2016 ), the ratio of numerical entities (Ferreira et al., 2013 ; Lin, 1999 ) and Cue words (Edmundson, 1969 ).

Gupta and Lehal, in their survey of text summarization techniques, list the following groups of features: content-based, title-based, location-based, length-based, proper noun and upper-case word-based, font-based, specific phrase-based, and features based on sentence similarity to other sentences in a text (Gupta & Lehal, 2010 ). Previous studies use different sentence features such as terms from keywords/keyphrases and user queries, frequency of words, and position of words/sentences for text summarization (Ozsoy et al., 2011 ).

However, in most cases, the selection and weighting of features are a crucial matter of debate. Some works have been carried out with respect to this (Neto et al., 2002 ), but none, to the best of our knowledge, has shown that the target attribute is highly localized within the context of the document. It is occasionally mentioned but not included in practice. For instance, Ferreira et al. studied various combinations of sentence scoring methods on three types of documents (Ferreira et al., 2013 , 2014 ). They concluded that the weight of features varies, depending on the properties of context: “the effectiveness of sentence scoring methods for automatic extractive text summarization algorithms depends on the kind of text one wants to summarize, the length of documents, the kind of language used, and their structure”. Yeh et al. ( 2005 ) utilized a Genetic Algorithm (GA) to find the weight of features for calculating sentence scores. However, their following statement implies that performance of weights is generally dependent on genre, which could be seen as a feature of context: “It cannot be guaranteed that the score function whose feature weights are obtained by GA definitely performs well for the test corpus; nevertheless, if the genre of the test corpus is close to that of the training corpus, we can make a prediction that the score function will work well.” (Yeh et al., 2005 ). Berenjkoub et al. studied the effectiveness of various subsets of features in the summarization of distinct sections of scientific papers (Berenjkoub & Palhang, 2012 ). They showed that some features work well only in some specific portions of text, for example, in the abstract section, while others perform better in the methodology section. This locality effect could be considered a consequence of differences in the structure and context of each section.

All the above studies imply the significance of document context in ranking. Nevertheless, it has not been given enough attention in the NLP community and even sometimes is neglected. For instance, Dlikman ( 2016 ) suggests using a wide range of various features. Among these, seventeen part-of-speech-based sentence features have been introduced, which are all sentence-normalized, not document-normalized, i.e. they count the ratio of a syntactic unit, like verbs, divided by the number of words in a sentence. However, such features do not consider the total number of those units, e.g. verbs, in the whole document. Our work contributes to this line of research and includes document features in the learning and ranking processes.

With regard to evaluating the results, apart from prevalent measures like ROUGE, which compare system summaries with reference summaries, there are others, such as FRESA (Saggion et al., 2010 ; Torres-Moreno et al., 2010 ) which evaluates the quality of summaries without human summaries. In our experiments, we assessed the results with and without human references using these methods.

3 Incorporating document features

As a way to investigate the need for document features in sentence ranking (as explained in the introduction and literature overview), we introduced several document-level characteristics and incorporated them into the summarization process. These features are listed under Sect. 3.1.1 . Although stages of our method do not differ from state-of-the-art supervised extractive summarization, the whole process is explained to clarify and investigate the method.

Every supervised summarization has two phases. Firstly, the “Learning Phase” uses a corpus of ideal summaries to train the system to rank sentences. Secondly, in the “Summarization Phase”, the system utilizes the learned model from the first phase in order to rank the sentences of a newly given text. Afterwards, the process of sentence selection is performed to form a summary of the given input. Each of these phases has several intricacies, which are briefly described in the following sections.

3.1 Learning phase

The input to this phase is a dataset of documents, each of which is associated with several human-written summaries. The output is a learned model with a good level of accuracy that is able to reliably predict the sentences' rank in almost the same way that a human might rank them. Performing normalization, sentence and word tokenization, and stop-word removal is essential. We explain the following subtasks that should be carried out later.

3.1.1 Feature extraction

Foremost, it is necessary to represent each sentence with those features that have the most distinguishing effect on the prediction of the rank. Many features have been examined in the literature. We call some “document-aware” because they implicitly represent some information about a document. However, other features that convey no information about the document have been used. We call these features “document-unaware”. In the previous sections, we argued that the lack of document-related information might be misleading for the summarizer system, especially when we train it with sample sentences from different documents. Thus, we modified some document-unaware features and derived new features that cover document properties. We also examined the effect of incorporating explicit features of a document into the vectors of its sentences. The following subsections describe the features mentioned above in more detail.

3.1.1.1 Document-unaware features

Ordinal position It is shown that the inclusion of a sentence in a summary is relevant to its position in the document or even in a paragraph. Intuitively, sentences at the beginning or end of a text are more likely to be included in the summary as they carry more information than the body of the text. Depending on how it is defined, the position feature might be either document-unaware or not. For example, in Fattah and Ren ( 2008 ) and Suanmali et al. ( 2009 ), it is defined as 5/5 for the first sentence, 4/5 for the second, and so on down to 1/5 for the fifth and zero for the remaining sentences. Another research conducted by Wong et al. ( 2008 ) defines it as 1/sentence number. With such a definition, we may have several sentences. For instance, position = 1/5 in the training set, may not have the same sense of position. While a sentence position = 1/5 means “among the firsts” in a document with 40 sentences, it has a totally different meaning of “in the middle”, for another document that contains ten sentences. Thus, a helpful feature formula should distinguish the differences between documents that may change the meaning of its information. In our experiments, we used the definition of Wong et al. ( 2008 ). Furthermore, a document-aware version of this feature will be introduced in Sect. 3.1.1.2 .

Length of the sentence Intuitively, verbose or laconic sentences are less likely to be included in the summary. This is because verbose sentences undermine brevity, and laconic ones could diminish the richness of information whilst these two are prized in summarization. Like sentence position, this feature is susceptible to the misdefinition that makes it document-unaware. As an example, Wong et al. ( 2008 ) defined it as the number of words in a sentence. Such a definition does not consider the relative length of sentences to their surroundings; e.g., a sentence containing 15 words may be recognized as lengthy if other sentences in the document include fewer words.

The same sentence length might be treated as short if all other sentences in a document have more than 15 words—the root of this misinterpretation can be traced back to the writers’ distinctive styles. However, we investigate this feature in our experiments to compare its effect with its document-aware counterpart, which will be listed in Sect. 3.1.1.2 .

The ratio of nouns Is defined in Dlikman ( 2016 ) as the number of nouns divided by the total number of words in the sentence after stop-word removal. Three other features, ratio of verbs, ratio of adjectives, and ratio of adverbs, are defined in the same manner and have proved to have a positive impact on ranking performance. However, this feature does not capture the overall number of nouns inside a document. From our perspective, a sentence with a ratio of nouns = 0.5, for example, in a document containing many nouns, must be distinguished from another sentence in the training set with the same ratio of nouns that appeared in a document comprising fewer nouns. The same discussion justifies the need to consider the document's number of verbs, adjectives, and adverbs. The impact of these features is examined in our experiments and compared to their document-aware counterparts.

The ratio of numerical entities Assuming that sentences containing more numerical data are probably giving us more information, this feature may help us in the ranking (Ferreira et al., 2013 ; Lin, 1999 ). For calculation, it counts the occurrences of numbers and digits proportional to the length of the sentence. This feature does not take into account the numbers and digits in other sentences of the document, whereas it must be less weighted if almost all sentences of a document have numerical data. As a result, we must introduce a document-aware version of this feature.

Cue words If a sentence contains particular phrases such as “in conclusion”, “overall”, “to summarize”, “in a nutshell”, and so forth, its selection for the summary is comparatively more probable. The occurrence frequency of these phrases is calculated for this feature.

3.1.1.2 Document-aware features

Cosine position As mentioned in Sect. 3.1.1.1 , a good definition of position should reflect the length of document. A well- known formula used in the literature (Barrera & Verma, 2012 ; Verma & Filozov, 2010 ) is:

In which index is an integer representing the order of sentences and T is the total number of sentences in the document. This feature ranges from 0 to 1; the closer to the beginning or end a sentence is, the higher value this feature will take. Alpha is a tuning parameter. As it increases, the value of this feature will be distributed more equally over sentences. Consequently, equal values in the training set represent a uniform notion of position in a document.

Relative length The intuition behind this feature is explained in Sect. 3.1.1.1 . A simple word count does not reflect the relative length of a sentence compared to other sentences that appeared in the document. In that regard, we normalize it by dividing the number of words in the sentence over the average length of sentences in the whole document. More formally:

in which n is the number of sentences in the document and \({s}_{i}\) is the i’th sentence of it. Values greater than 1 could be interpreted as long and vice versa.

Term frequency × inverse sentence frequency TF-ISF counts the frequency of terms in a document and assigns higher values to sentences having more unique words. It also discounts terms that repeatedly appear in various sentences. Since it is explained thoroughly in the literature, we have not included the details and formula presented in references (Neto et al., 2000 , 2002 ). As TF-ISF captures the frequency and inverse sentence frequency of a term with respect to its context, we can classify it as a document-aware feature.

POS features We introduce a different procedure to include the ratio of the part of speech (POS) units in features and keep them document-normalized. We divide the number of occurrences of each POS unit by the document's total appearance instead of a sentence's total.

The formal definition of the new document-aware features is shown as follows:

3.1.1.3 Explicit document features

We defined several document-specific features in order to investigate further how effective they are in sentence ranking. These features of the document will be placed in every sentence's feature vector of that document. Their formal definition is described below, and their impact is examined in the result and discussion Sect.  5 :

Document sentences An essential quality of a document that affects summarization is the total number of sentences participating in sentence ranking. As this number grows, the summarization should be more selective and precise. Furthermore, some sentence features, like cue words, should be heavily weighted for longer documents. In addition, the main contextual information is distributed over sentences. Regarding this case, even nominal values of features should be considered meaningful.

Document words Another notion of document length is the number of document words. Because the number of sentences is inadequate to represent document length, we should put this feature into practice.

Topical category Different topics, such as political, economic, etc. have different writing styles, which affects sentence ranking. For instance, numerical entities appear more in economic texts or sports reports than in religious or social news. Therefore, the weight of this attribute should vary depending on a document’s category.

The example in Fig.  1 represents an overview of our feature set. The ID column is for enumeration, and the Target column is explained in the next section.

An excerpt of whole feature set. SC and SP under topical category stand for Science and Sport, respectively

3.1.2 Target assignment

Every feature vector needs a target value from which the system should learn how to rank sentences. The value of the target is usually determined based on golden summaries. If a sentence is included in most human-written extracts, its target is near 1. In contrast, it would be closer to 0 if the sentence could not be found in any human-made summaries. In some datasets, like Pasokh (Moghaddas et al., 2013 ), golden summaries are not entirely extractive, i.e. they are not composed of exact copies of sentences in the original text. Therefore, a measure of similarity between the sentences of the source text and each golden summary’s sentences will be calculated, which yields real values in the range of 0 to 1. Section  4 includes more details about the target assignment.

3.1.3 Training model

Since target attribute values vary between zero and one, we opted to use regression and classification methods for the learning task. Moreover, a global matrix in which rows correspond to corpus's sentences and columns correspond to features is composed to build a training and test set. The last column shows the target attribute, which will be omitted in the test set. It might be required to perform scaling on specific columns, depending on its corresponding features and range of its values.

For large datasets, the total number of sentences that are not included in golden summaries is numerous compared to included ones. Therefore, this leads to regression bias toward lower target values. Dataset balancing, leaving aside a portion of not included sentences and feeding the remaining to the learner model, is needed to mitigate the bias.

Lastly, the model should be fitted on the training set and be evaluated against a test set as described in Sects.  4 and 5 .

3.2 Summarization phase

In this section, we briefly describe the summarization process. The evaluation process is explained in Sect.  3.3 .

3.2.1 Feature extraction

Initially, sentence features need to be extracted. Normalization, sentence tokenization, word tokenization, and stop-words removal are preliminary steps. Also, the same features used in the learning phase should be calculated.

3.2.2 Sentence ranking

In comparison with the learning phase, in which a global matrix was used, a local matrix is composed whose rows correspond with the sentences of the input text. Moreover, the same scaling procedure as the learning phase should be carried out. The matrix is then fed to the regressor obtained in the previous stage to predict a rank value between zero and one for each sentence.

3.2.3 Sentence selection

The most appropriate sentences for being included in the summary will be determined by sorting sentences based on their ranks. However, it is essential to preserve original sentences order to enhance readability.

Another consideration is the cut-off length, i.e., how many of the top sentences should we select for the summary? The answer should be as simple as a constant number, a percentage of total sentences, or more advanced heuristics could determine it. We allowed cut-off length to be an input parameter, which enables us to, in the evaluation phase, produce summaries of the same length as golden summaries. Consequently, it makes the comparison more equitable.

3.3 Evaluation measures

In this section, some measures are described to evaluate the performance of both phases explained in the previous section: the learning and summarization phases. The former is evaluated using standard regression metrics such as mean square error (MSE) and coefficient of determination (R 2 ). The latter is carried out using ROUGE, which is a well-known metric for evaluating summarization systems.

Mean square error (MSE) is the average of squared errors in all estimated targets. An ideal regressor tends to make this measure as near as possible to zero. However, an exact zero for MSE is not desirable because it is suspected of overfitting.

The coefficient of determination is another metric for evaluating how well a regression model is fitted to data. It ranges from -∞ to 1. As it approaches 1, “goodness-of-fit” is increased, while negative values show that the mean of data is a better estimator for the target (Nagelkerke, 1991 ).

ROUGE is proposed in Lin ( 2004 ) as an evaluation metric for summaries. It matches n-grams in system-produced and reference summaries and returns the percentage of matches in terms of precision, recall and f-measure. A variety of ROUGE family metrics, namely ROUGE-1, ROUGE-2, and ROUGE-L, have been proposed in the literature. ROUGE-1 calculates the overlap of 1-g, ROUGE-2 the bigrams, and ROUGE-L the Longest Common Subsequence (LCS) to measure resemblance. Nevertheless, we found that ROUGE assessments are always relatively high, even for a perfunctorily produced summary. Hence, we designed a random summarizer as a baseline for comparison that selects random sentences for the summary and evaluates using ROUGE.

Evaluation without reference summaries is beneficial, especially for enormous datasets where it is impossible to get human summaries of all texts. These approaches typically compare system summaries with the documents themselves or other systems' results. Jensen Shannon Divergence (JSD) is an information-theoretic method based on the distribution of words in the original texts and system summaries. Louis and Nenkova ( 2009 ) examined several such measures and concluded that JSD is the best measure in this regard. A simple implementation of JSD is published in Ruder and Plank ( 2017 ), which we used in our evaluation.

4 Experiments

We set up two experiments to verify our hypothesis that sentence ranking is highly dependent on the document and contextual features. These experiments evaluate how effective our method, exploiting document-aware features for summarization, is against the more commonly practiced method of using document-unaware counterparts.

The first experiment involves document-unaware features (listed in Sect.  3.1.1 ) alongside TF-ISF. In the second experiment, document-aware features were used instead of document-unaware ones. Furthermore, we set up a random summarizer based on a random regressor that acts as a baseline for comparisons. More details are recorded in Sect.  4.4 .

Moreover, we tried to find similar systems to compare our method with. However, there is hardly an available Persian summarization system comparable to ours. For example, Farahani et al. ( 2021 ) has leveraged BERT for this task, which is not comparable to our method because it’s abstractive and thus, measuring overlap of their summaries with reference extractive summaries doesn’t yield comparable results. We faced the same problem for many other published papers. Nevertheless, Asgarian ( 2021 ) has revised the TextRank (Mihalcea & Tarau, 2004 ) algorithm and provided a web service. Thus, we used its API to compare our results with their method.

4.1 Pasokh dataset

We used the Pasokh dataset (Moghaddas et al., 2013 ), which contains 100 Persian news documents, each associated with five summaries. Each summary consists of several sentences of the original text selected by a human expert. Some sentences are slightly modified; therefore, they are not an exact copy of the original sentences. Pasokh’s documents are categorized into six sections: political, economic, sport, science, social, and cultural, which has been reflected in the file name of documents. The length of documents ranges from 4 to 156 sentences, and it has about 2,500 sentences overall.

4.2 Extracting features and scaling

All features introduced in Sect.  3.1.1 are calculated. Pre-processing, sentence and word tokenization, stop-word removal, and part of speech tagging are performed using the Hazm library (Hazm, 2019 ), whose performance and effects on the process are evaluated in the 4.2.1. The list of stop words is determined from a GitHub repository. Footnote 1 After those steps, the majority of features have a range between zero and one. Other features are passed to a min–max scaler to transform into the same range. For the category feature, which is nominal, the one-hot-encoding method was applied, and six flag features were used.

4.2.1 Hazm toolkit

While English has many processing toolkits, such as NLTK and CoreNLP, Persian libraries are mostly scarce and premature. In such circumstances, the Hazm toolkit (Hazm, 2019 ) has proven very useful. It supports preprocessing and processing of Persian language, such as tokenization, stemming, POS tagging, dependency parsing, etc. It performs moderately well in all these tasks but stemming. Therefore, we didn’t perform stemming but used the original form of words in our experiments.

4.3 Target assignment

In the target assignment, as mentioned in Sect.  3.1.2 , the goal is to associate a number between 0 and 1 with higher values indicating the presence of a sentence in the majority of golden summaries. Because exact matching between sentences is not possible, to resolve the question of presence in a single golden summary such as g, we calculated the cosine similarity of the desired sentence with each sentence: \({s}_{j}\in g\) . Then the maximum value of these similarities is selected as an indicator of presence. This indicator is then calculated for other golden summaries, and their average is assigned to the sentence as the target:

G is a set of summaries written for the document containing s. This formula is additional explicit evidence that the target (and subsequently, ranking) is related to the document.

4.4 Training model

A vast collection of scikit-learn tools was used for the learning phase. K-fold cross-validation is applied with k = 4 and a split size of 0.25. Three different regression methods were applied, including Linear Regression, Decision Tree Regression, and Epsilon-Support Vector Regression. Overall results were the same, with minor differences. Thus, only the SVR result is reported. Various values for parameters were examined but the best results were achieved by epsilon = 0.01, kernel = rbf, and default values for other parameters. The fitted regressor of each run was used to rank documents’ sentences in the test set to evaluate summary qualities. The produced summary should have the same number of sentences as the counterpart standard summary to have a fair comparison. Therefore, we generated system summaries, conforming to the sentence count constraint, and compared them with ROUGE. Averaging these ROUGE scores over each document and then over the dataset will indicate the overall quality of model-produced summaries.

The same process was repeated with a random regressor that needs no training and assigns a random number between zero and one to any given sample. Apart from measuring the performance of this regressor on the test set, the quality of summaries produced is evaluated and reported as a baseline. The juxtaposition of this baseline and our measured results will demonstrate how effective our feature set is and how intelligent our whole system works.

5 Results and discussion

In Sect.  3.3 , MSE, R 2 , and ROUGE scores are noted as evaluation measures. The results of our experiments are reported below in terms of these measures. We also ran another experiment in which the random regressor was used for ranking sentences and producing summaries for better comparison. Table 1 shows and compares MSE and R 2 reported from these experiments.

The results show that in experiment 2, the mean squared error is reduced, and the R 2 score is increased. As a result, it proves that using document-aware features leads to a more accurately learned model, confirming our hypothesis about the relationship between document features and ranks.

The JSD results are displayed in Table  2 . The closer the JSD value to 1 is, the better similarity has been found between the distribution of words in produced summaries and corresponding original documents. The table reveals that the first experiment showed better performance from the vantage point of evaluation without reference summaries. It can be attributed to the fact that unnecessary information might be repeated in documents and in low-quality summaries. In other words, a low-quality summary might contain repetitive information, but gain high JSD values because it mimics the same distribution of words as it is in the original texts.

Finally, the ROUGE scores are displayed separately in terms of precision, recall, and f-measure in Figs. 2 , 3 and 4 , respectively. F-measure scores are shown in Fig.  2 , comparing ROUGE-1, ROUGE-2, and ROUGE-L. Figures  3 and 4 allow the comparison of precision and recall scores. The higher values gained in experiment 2 confirm that document-aware features perform better than unaware features.

ROUGE quality of produced summaries in terms of f-measure

ROUGE quality of produced summaries in terms of precision

ROUGE quality of produced summaries in terms of recall

These results are also interpretable from the viewpoint of entropy-based decision tree methods. In the learning phase, the impurity (Gini index) of features within the whole dataset will be measured, and features having higher information gain will be placed in the upper levels of the tree. But in the summarization phase, within which decisions have to be made within a single document, the impurity of those features may be low, resulting in less effective decisions and precisions. We help the model to use different features (thus different trees) for different documents by incorporating document features.

Another insight from these charts is that a random summarizer resulted in more than 50% scores in all measures. Without using document-aware features, the model achieves a slight improvement over a random summarizer.

6 Conclusion

This paper has discussed that we cannot learn to rank, in supervised extractive summarization by considering dataset sentences as independent educational examples. The rank of sentences is dependent on each other within a document. To overcome this issue, we suggested incorporating document features explicitly in a feature vector of sentences. We also suggested using features that take into account the properties of the document, document-aware features. Conducted experiments demonstrated the benefit of adding explicit document features and document-aware features, both in model precision and summary quality.

For future work, more document-aware features can be examined. For example, the position of a sentence in the paragraph seems worthy of investigation, which might be effective because paragraphs tend to have a single topic sentence and possibly a concluding sentence. They are more likely to be selected for the summary. Nevertheless, paragraph sentence position is not a reasonable choice across all languages. For instance, in Japanese, the notion of the paragraph is somehow replaced with Danraku (Kimura & Kondo, 2004 ), and it does not necessarily include a topic or concluding sentences.

If available, it is also possible to run the same experiments on any other language dataset. Since the features we used are based on words, sentences, and POS tags, our method is not language-specific and can be easily applied to other languages. Nonetheless, for some languages, this might not be the case. For example, in the Thai language, sentence ending markers are not explicit (Charoenpornsawat & Sornlertlamvanich, 2001). Thus, the whole idea of sentence ranking and selection faces an essential preliminary challenge of sentence tokenization, which falls beyond the scope of this paper.

Measuring the degree of entropy difference between dataset and single documents in a standard dataset can be investigated as future work. Suppose the entropy of a feature in the whole dataset is significantly different from its average entropy in each document. In that case, the feature is not applicable, and it needs interventions similar to this study.

The results of our study, though conducted before introducing Large Language Models and ChatGPT, Footnote 2 are still valid and useful. A recent study has shown that ChatGPT achieves lower performance compared to state-of-the-art extractive approaches (Zhang et al., 2023 ). It also has demonstrated that output of extractive methods can be used as guidance for improving the performance of ChatGPT in abstractive summarization. Thus, improvements in extractive methods are still worthy of research.

Our source code is hosted on GitHub Footnote 3 and is published for later reference, further experiments and reproducing results. A web interface Footnote 4 and a Telegram bot Footnote 5 is also implemented as a demo of our method.

A subset of https://github.com/kharazi/persian-stopwords/blob/master/short .

The main body of this study is conducted in 2017 and 2018, and the submission started on 2019, however the publication is going to happen in 2024.

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Rezaei, H., Mirhosseini, S.A.M., Shahgholian, A. et al. Features in extractive supervised single-document summarization: case of Persian news. Lang Resources & Evaluation (2024). https://doi.org/10.1007/s10579-024-09739-7

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How to Summarize Research Papers and articles using AI?

Shaurya Bedi

Research papers can be difficult due to their inherent complexity and technical nature. The volume of technical terms and jargon may be a barrier, making it more difficult for readers to understand the content.

Sometimes, you must feel overwhelmed, not knowing everything!

Furthermore, research papers usually delve into intricate theories, models, and statistical analyses, necessitating a solid foundational knowledge of the field to guarantee appropriate comprehension. What complicates the problem further is the size of the research papers and the need to assess the supplied data critically.

Understanding the insights from a research paper doesn't have to feel like deciphering a cryptic code. 

Thanks to the strides in artificial intelligence, distilling the essence of a research article has become remarkably straightforward. 

Whether you're a student crafting your paper, an individual eager to unravel the depths of a lengthy article, or someone intrigued by a complex subject, using AI study assistants to summarize article is a good idea and offers a simplified solution to grasp the core ideas without going through intricate scientific jargon.

Let's dive in to see how to summarize research article.

Overview of AI for research papers and articles

AI tools are like competent helpers for researchers. They're trained to understand language and do things like humans. AI makes things easier in  literature reviews  by processing information, finding important studies, and making quick summaries. 

It's a significant change, helping researchers read, analyze, and compare articles effortlessly. Using language skills, AI simultaneously spots vital ideas and findings from many articles, saving researchers time and effort. It's like a super helper for understanding lots of information in literature.

In simple terms, old summarizing methods can make mistakes, like getting things wrong or leaving out important stuff. But with the AI research paper summarizer, using super-intelligent algorithms, it can find and pull out the main points from papers without making mistakes. 

This makes sure the AI summarizer research paper is right on the money, making it an excellent tool for getting research paper summaries spot-on.

Benefits of Summarizing Research Papers and Articles Using AI

Using AI to summarize  research papers  and articles is a game-changer. It saves time, makes things easier to understand, and helps students and professionals quickly get the important stuff. Now, let's see how.

Time saved in information processing

Reading tons of articles the old-fashioned way takes forever! Researchers spend weeks or months sorting through articles, reading them, and picking out the important stuff. It's a big job, especially with lots of studies.

But guess what? AI makes it way faster and easier; with excellent tools and intelligent algorithms, AI can zip through many research articles and give short summaries. That means less time slogging through details and more time for researchers to dig into meaningful sections. 

Better understanding and retention

Keeping up with tons of new research is challenging. There's so much out there; staying on top of it all is hard.

But guess what? AI is here to help! With smart tools, it can check out new sections for you. That way, researchers can always be in the loop with the latest and greatest in their field. Thanks to article summarizer AI, keeping your brains filled with the newest information is more accessible.

Simplified information retrieval

AI gets the tricks in understanding language and learning from machines. It snags info from many places, organizes it, and finds the perfect answers for you, even if your search could be clearer.

Personalized and precise summaries

Incorporating AI tools into literature reviews decreases the likelihood of human errors in traditional methods or manual summarization. By doing so, these AI tools enhance the efficiency and accuracy of literature reviews, enabling researchers to access pertinent information while minimizing errors swiftly.

Comprehensive content insights

AI takes the heavy lifting out of content analysis. Instead of spending hours dissecting articles and papers, researchers can rely on AI to swiftly analyze and provide comprehensive insights. It digs into the details, identifies key themes, and helps researchers grasp the complete picture without exhaustive manual effort.

Streamlined decision-making

For research, quick decision-making is crucial. AI-powered summarization accelerates this process by distilling complex information into concise summaries. Researchers can make informed decisions faster, whether it's about the relevance of a paper, the direction of their own research, or the incorporation of new findings into their work.

Language and context adaptability

Different fields have their own jargon and nuances, and navigating and understanding diverse research papers is challenging. AI comes to the rescue with its ability to adapt to various languages and contexts. It breaks down barriers by providing summaries catering to the researcher's specific language and context, ensuring seamless information integration across disciplines.

Steps to Summarize Research Papers and Articles Using AI

Here are some steps you need to know if you are wondering how to summarize a research article using AI:

Selecting the Right AI-Powered Tool or Software

Choosing the right AI tool is the first step to efficient summarization. Researchers should explore and select a tool that aligns with their specific needs and the nature of their research. For example, tools like SummarizeBot or  IBM Watson  can be tailored to different domains, ensuring a customized and effective summarization process.

Inputting and processing text or documents

Once you select the tool, the next step involves feeding it with the relevant text or documents. Researchers can upload the research papers or articles they want to summarize. 

This input is then processed through the AI algorithms, which work their magic to extract critical information and generate concise summaries. It's a straightforward process that significantly reduces the manual effort required for traditional document analysis.

Adjusting settings and preferences

AI to summarize article often comes with customizable settings to tailor the summarization output. Researchers can adjust preferences based on factors such as the desired summary length or the level of detail required. 

For instance, settings can be adjusted to generate shorter summaries if a quick overview is needed. This flexibility allows researchers to fine-tune the summarization process according to their preferences, enhancing the tool's adaptability to individual needs.

Generating and reviewing summaries

The final step involves generating and reviewing the AI-generated summaries. Researchers receive concise overviews of the key points from the inputted documents. It's a time-saving approach that lets them quickly grasp the core ideas without delving into the entire content. 

After receiving the summaries, researchers can review and ensure the accuracy and relevance of the information. This step ensures that the AI-generated summaries align with their research goals and objectives.

Why Outsourcing AI-Driven Summarization to a Virtual Assistant is Beneficial

Virtual assistants possess expertise in utilizing various AI tools, ensuring optimal selection and configuration for effective summarization.

Let's see why it is beneficial:

Expertise in AI tools and applications

Virtual assistants are well-versed in utilizing a variety of AI tools and applications. Their expertise ensures optimal selection and configuration of tools, aligning with the specific requirements of the research. 

For example, a virtual assistant proficient in tools like OpenAI's GPT-3 can deliver advanced summarization capabilities, enhancing the overall quality of the process.

Quality control and accuracy

Virtual assistants bring a human touch to AI-driven summarization by overseeing quality control and accuracy. They can review and refine the AI-generated summaries, ensuring they meet the desired standards. This extra layer of human scrutiny enhances the reliability of the summarized content, addressing any potential inaccuracies that may arise during the automated process.

Handling large volumes of information

Dealing with a large volume of research papers can overwhelm individual researchers. Virtual assistants excel in managing such volumes efficiently. Their ability to process information at scale ensures that researchers can obtain summaries for numerous documents without compromising accuracy or speed.

Customization and personalization

Virtual assistants offer a personalized touch to the  AI to summarize article  process. They can adapt to researchers' preferences, adjusting settings and tailoring summaries to meet specific criteria. 

This customization ensures that the summarization output aligns with the unique needs and goals of the researcher, providing a tailored and efficient solution.

Focus on core research goals

Outsourcing summarization allows researchers to concentrate on their core research goals and activities, enhancing overall productivity and output.

Other AI-Related Tasks a Virtual Assistant Can Help You With

Virtual assistants are versatile and can assist with various AI-related tasks, including the following:

Data Analysis and Interpretation

Virtual assistants adept in data analytics can assist researchers in interpreting complex datasets. They streamline the analysis process, extracting meaningful insights and trends. 

For example, a virtual assistant skilled in tools like Tableau or Python can facilitate data-driven decision-making, enhancing the researcher's understanding of their findings.

AI Model Training and Optimization

Proficient in AI model training, virtual assistants contribute to refining and optimizing models for specific research needs. They can work with frameworks like TensorFlow or PyTorch, ensuring that the AI models align with the desired outcomes. This collaborative effort enhances the accuracy and performance of AI applications within the research domain.

Travel Planning

Beyond technical tasks, virtual assistants excel in practical aspects like travel planning. When you use AI algorithms, they can identify optimal travel routes, accommodations, and schedules. This capability is particularly beneficial for researchers attending conferences or fieldwork, ensuring seamless and efficient travel logistics.

Proofreading

Ensuring the quality of written content is crucial in research. Virtual assistants equipped with language processing capabilities can aid in proofreading, identifying grammatical errors, and enhancing the clarity of research papers. This attention to detail contributes to the overall professionalism and impact of the research output.

Content and Marketing Strategy

Virtual assistants with knowledge of AI in content creation and marketing can assist in developing strategies. They can analyze trends, identify target audiences, and recommend content optimization techniques. 

For example, a virtual assistant familiar with SEO principles can enhance the visibility of research outputs, contributing to a broader impact.

Why is Wishup the best place to hire a Virtual Assistant?

Choosing the right place for hiring a virtual assistant is crucial for optimal support.  

Wishup  stands out as the premier choice for several compelling reasons.

We hire only the top 0.1% of applicants

Wishup's stringent selection process ensures that only the top 1% of applicants are hired as virtual assistants. This commitment to excellence guarantees that clients receive assistance from highly skilled and competent professionals.

Onboarding in 24 hours

Time is of the essence, and Wishup recognizes this. With a swift onboarding process, clients can have a virtual assistant ready to support them within just 24 hours of initiating the hiring process.

Choose from US-based or Indian VAs

Wishup offers the flexibility to choose virtual assistants based on preferences, whether clients prefer US-based or Indian professionals. This versatility caters to diverse needs and ensures a seamless collaboration experience.

Pre-trained and upskilled professionals

Wishup's  virtual assistants  come pre-trained and continually upskilled to stay abreast of the latest trends and technologies. This dedication to ongoing professional development ensures that clients benefit from the most proficient and updated virtual assistant support.

Dedicated Account Manager at your service

Clients enjoy personalized assistance with a dedicated Account Manager from Wishup. This point of contact ensures effective communication, understanding of client needs, and seamless coordination for a more tailored and responsive virtual assistant experience.

Instant replacement policy

Wishup prioritizes client satisfaction with its instant replacement policy. In case of any concerns or preferences for change, clients can swiftly access a replacement, ensuring uninterrupted support and a hassle-free engagement.

Conclusion 

In conclusion, AI summarization tools revolutionize academic research by simplifying the literature review. Researchers benefit from staying current with field advancements and making well-informed decisions. 

Delegating the task to a Virtual Assistant enhances efficiency and precision, primarily through platforms like Wishup. The advantages are clear: time-saving, better understanding, and personalized summaries. 

Ready to experience these benefits? Take the next step by  scheduling a free consultation  or emailing [email protected] . Elevate your research game with AI and virtual assistance – your academic journey just got much smoother!

AI to summarize article: Frequently Asked Questions 

Can i use ai to summarize articles.

Yes, AI excels at summarizing articles, offering a quick and efficient way to distill key information from lengthy content.

Can ChatGPT summarize articles?

Yes, ChatGPT, powered by OpenAI, is adept at summarizing articles. Its language understanding capabilities make it a valuable tool for concise content extraction.

Can OpenAI summarize an article?

Yes, indeed. OpenAI provides powerful models like GPT-3, which can effectively summarize articles, offering a comprehensive overview of the content.

Which AI is good for summarizing?

Several AI models, including GPT-3, BERT, and SummarizeBot, excel in summarization. The choice relies on your specific requirements, such as language nuances, length preferences, and application context.

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