5 AI tools for summarizing a research paper

5 AI tools for summarizing a research paper

The inherent intricacy and technical nature of research papers’ content make reading them a challenging undertaking. These research articles can be difficult to understand, especially for non-experts or those who are new to the area because they frequently contain specialized vocabulary, complicated concepts and complex methodologies. The amount of jargon and technical terms might act as a barrier, making it harder for readers to comprehend the content.

Additionally, research papers frequently dive into complex theories, models and statistical analyses, demanding a solid background understanding of the subject to ensure adequate comprehension. The voluminous nature of the research papers and the requirement to critically evaluate the provided data only make the issue worse.

As a result, it could be difficult for readers to distill the key points, determine the significance of the findings, and combine the data into a coherent perspective. It frequently takes persistence, the incremental accumulation of domain-specific knowledge and the creation of efficient reading techniques to get beyond these obstacles.

Artificial intelligence (AI)-powered tools that provide support for tackling the complexity of reading research papers can be used to solve this complexity. They can produce succinct summaries, make the language simpler, provide contextualization, extract pertinent data, and provide answers to certain questions. By leveraging these tools, researchers can save time and enhance their understanding of complex papers.

But it’s crucial to keep in mind that AI tools should support human analysis and critical thinking rather than substitute for them. In order to ensure the correctness and reliability of the data collected from research publications, researchers should exercise caution and use their domain experience to check and analyze the outputs generated by AI techniques.

Here are five AI tools that may help summarize a research paper and save one’s time.


ChatGPT plays a crucial role in summarizing research papers by extracting key information, offering succinct summaries, demystifying technical language, contextualizing the research and supporting literature reviews. With ChatGPT’s assistance, researchers can gain a thorough understanding of papers while also saving time.

  • Extrapolating key points: ChatGPT can analyze a research article and pinpoint its core ideas and most important conclusions. It might draw attention to crucial details, including the goals, methods, findings and conclusions of the study.
  • Information condensation: ChatGPT can provide succinct summaries of research papers that perfectly capture their main points by processing their text. It can condense large sentences or sections into shorter, easier-to-read summaries, giving a summary of the main points and contributions of the paper.
  • Simplifying technical terms: Technical terms and sophisticated terminology are frequently used in research papers. To make the summary more understandable to a wider audience, ChatGPT can rephrase and clarify these terms. It may offer explanations in simple terms to aid readers in comprehending the material.
  • Contextualizing: ChatGPT can contextualize the research paper by connecting it to prior understanding or highlighting its significance within a larger body of research. Giving readers a thorough knowledge of the paper’s significance, it may include background information or make links to pertinent theories, studies or trends.
  • Handling follow-up questions: Researchers can communicate with ChatGPT to ask specific questions regarding the research paper in order to get more information or elaborations on certain points. Based on its knowledge base, ChatGPT can offer extra details or insights.

Related: 10 ways blockchain developers can use ChatGPT


QuillBot offers a range of free tools that empower writers to enhance their skills. Both ChatGPT and QuillBot can be used together. When using ChatGPT and QuillBot in conjunction, begin with ChatGPT’s output and paste the output into QuillBot. 

QuillBot then analyzes the text and offers suggestions to enhance readability, coherence and engagement. One has the freedom to decide between many writing styles, including expansive, imaginative, straightforward and summarized. To further personalize the text and give it a distinct voice and tone, users can change the sentence structure, word choice and overall composition.

QuillBot’s Summarizer tool can help break complex information into digestible bullet points. To understand a research paper, one can either directly input the content into QuillBot or collaborate with ChatGPT to generate a condensed output. Afterward, they can utilize QuillBot’s Summarizer to further summarize the generated output. This streamlined approach allows for efficient summarization of the research paper. 


SciSpacy is a specialized natural language processing (NLP) library with an emphasis on scientific text processing. It makes use of pre-trained models to identify and annotate relationships and entities that are particular to a given domain.

It also contains functionalities for sentence segmentation, tokenization, part-of-speech tagging, dependency parsing and named entity recognition. Researchers can obtain deeper insights into scientific literature by using SciSpacy to streamline their analysis and summarizing procedures, extract important data, find pertinent entities and discover relevant things.

IBM Watson Discovery

An AI-powered tool called IBM Watson Discovery makes it possible to analyze and summarize academic publications. It makes use of cutting-edge machine learning and NLP techniques to glean insights from massive amounts of unstructured data, including papers, articles and scientific publications.

In order to comprehend the context, ideas and links inside the text, Watson Discovery employs its cognitive capabilities, which enable researchers to find unnoticed patterns, trends and connections. It makes it simpler to navigate and summarize complicated research papers since it can highlight important entities, relationships and subjects.

Researchers can build unique queries, filter and categorize data, and produce summaries of pertinent research findings using Watson Discovery. Additionally, the program includes extensive search capabilities, allowing users to conduct exact searches and obtain certain data from enormous document libraries.

Researchers may read and comprehend lengthy research papers faster and with less effort by utilizing IBM Watson Discovery. It offers a thorough and effective technique to find pertinent information, learn new things and make it easier to summarize and evaluate scientific material.

Related: 5 real-world applications of natural language processing (NLP)

Semantic Scholar

Semantic Scholar is an AI-powered academic search engine that uses machine learning algorithms to comprehend and analyze scholarly information.

To provide thorough summaries of the research publications’ primary conclusions, Semantic Scholar collects important data from them, including abstracts, citations and key terms. Additionally, it provides tools like subject grouping, related research recommendations and citation analysis that can help researchers find and summarize pertinent literature.

The platform’s AI features allow it to recognize significant publications and well-known authors and develop research trends within particular subjects. Researchers wishing to summarize a particular area of research or keep up with the most recent developments in their field may find this to be especially helpful.

Researchers can read succinct summaries of research publications, find relevant work and gain insightful information to support their own research efforts by utilizing Semantic Scholar. For academics, researchers and scholars who need to quickly summarize and navigate through voluminous research literature, the tool is invaluable.

Precaution is better than cure

It’s crucial to keep in mind that AI tools may not always accurately capture the context of the original publication, even though they can help summarize research papers. Having said that, the output from such tools may serve as a starting point, and one can then edit the summary using their own knowledge and experience.