Extractive Text Summarization: An Application Based Study

Year : 2024 | Volume : 11 | Issue : 02 | Page : 41 48
    By

    Deepanshu Anand,

  • Yugansh Gupta,

  • Arnav Sabharwal,

  • Vinay Kumar Saini,

  • Anshu Khurana,

  1. Student, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
  2. Student, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
  3. Student, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
  4. Associate Professor, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
  5. Assistant Professor, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

Abstract

Text summarization is an essential tool for extracting important information from lengthy texts or documents. Text Summarization has two main methodologies namely: Extractive Summarization and Abstractive Summarization. This study concentrates on extractive summarising, which selects significant sentences straight from the source material to create a summary. It is a popular option for many practical applications since it frequently produces summaries that are more accurate in terms of substance. In abstractive summarization, the summaries are generated by using the words that are not in the original text. However, the disadvantage of the above technique lies in the areas, where we want to retain the original text from the source. Hence the need of extractive summarization arises. Although, there are few drawbacks associated to extractive summarization which include the possibility of repetition and the dependence on pre-existing content. This study investigates how extractive summarization could be used to create end-to-end applications that are broadly applicable across various applications like legal document analysis. Through the resolution of these constraints and the utilisation of advances in natural language processing, extractive summarization could provide beneficial outcomes for a range of applications.

Keywords: Text summarization, supervised learning, unsupervised learning, semantics, extractive summary

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Deepanshu Anand, Yugansh Gupta, Arnav Sabharwal, Vinay Kumar Saini, Anshu Khurana. Extractive Text Summarization: An Application Based Study. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):41-48.
How to cite this URL:
Deepanshu Anand, Yugansh Gupta, Arnav Sabharwal, Vinay Kumar Saini, Anshu Khurana. Extractive Text Summarization: An Application Based Study. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):41-48. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155865


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 24/05/2024
Accepted 06/06/2024
Published 10/07/2024
449

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