Automatic Text Summarization

  • Unique Paper ID: 151847
  • Volume: 8
  • Issue: 1
  • PageNo: 982-988
  • Abstract:
  • The objective of this briefing is to propose an application that simplifies the process of extracting meaning from large documents and websites, by making the use of text summarization, i.e., extracting only meaningful excerpts from a large document or website. The Internet is amassed with text data that spans hundreds of pages, sometimes even greater, that requires its meaning to be extracted to both better help discover relevant information and to consume relevant information faster. This briefing proposes an application for the same, in two paradigms i.e., a web-based application, and a Chrome extension. This is an AI based application, using state-of-the-art technologies such as NLP. Two algorithms are proposed for the solution, Abstractive and Extractive Text Summarization; and their results are compared for a sample web page and a document, along with a user-specified web page and document respectively. Along with a dedicated backend occupied by NLP model, a middleware would also be developed as a part of the application, whose sole purpose is the preprocessing of web page as well as documents. At the end of the briefing, the deployment strategies of the application would be discussed, along with the limitations encountered while designing the application. The briefing would be concluded with the future works that are needed with the application, along with the references used in the development of this briefing.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{151847,
        author = {Vishal Tyagi and Utkarsh Dwivedi and Yash Kumar Sharma and Tarun Agarwal},
        title = {Automatic Text Summarization},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {982-988},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151847},
        abstract = {The objective of this briefing is to propose an application that simplifies the process of extracting meaning from large documents and websites, by making the use of text summarization, i.e., extracting only meaningful excerpts from a large document or website. The Internet is amassed with text data that spans hundreds of pages, sometimes even greater, that requires its meaning to be extracted to both better help discover relevant information and to consume relevant information faster. This briefing proposes an application for the same, in two paradigms i.e., a web-based application, and a Chrome extension. This is an AI based application, using state-of-the-art technologies such as NLP. Two algorithms are proposed for the solution, Abstractive and Extractive Text Summarization; and their results are compared for a sample web page and a document, along with a user-specified web page and document respectively. Along with a dedicated backend occupied by NLP model, a middleware would also be developed as a part of the application, whose sole purpose is the preprocessing of web page as well as documents. At the end of the briefing, the deployment strategies of the application would be discussed, along with the limitations encountered while designing the application. The briefing would be concluded with the future works that are needed with the application, along with the references used in the development of this briefing.},
        keywords = {AI (Artificial Intelligence), DL (Deep Learning), DRM (Device Rights Management), HTML (Hyper Text Markup Language), HTTP (Hyper Text Transfer Protocol), NLP (Natural Language Processing), NLTK (Natural Language Tool Kit), XML (Extensible Markup Language)},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 8
  • Issue: 1
  • PageNo: 982-988

Automatic Text Summarization

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