Published April 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Analysis and Implemented on Automated Text Summarization using Transformer Model

Creators

  • 1. Research Scholar, Department of Computer Science and Engineering, Sant Gadge Baba Amravati University, Amravati (M.H), India.

Contributors

  • 1. Research Scholar, Department of Computer Science and Engineering, Sant Gadge Baba Amravati University, Amravati (M.H), India.
  • 2. Associate, Professor and Head, Department of Information Technology, Anuradha College of Engineering, Chikhli, Sant Gadge Baba Amravati University, Amravati (M.H), India.
  • 3. Professor and Head, Department of Computer Science and Engineering Sant Gadge Baba Amravati University, Amravati (M.H), India.

Description

Abstract: Despite the fact that the work on automatic text summarization initially began 70 years prior, it has seen a remarkable development in the recent years due to new and advanced technologies. With the increasing significance of time, the need of condensed and precise information is on peak. No one has time to go through all the articles to get the right data. With the help of automatic text summarizer, we can shorten the source text while maintaining its data and overall meaning, thus saving time of the reader. Text summarization can extensively be alienated into two classifications, Abstractive Summarization also Extractive Summarization. Extractive summarization goals at distinguishing the foremost vital info that is at that moment separated and assembled system to a condensed summary. Abstractive summary group includes rewriting the complete article and the summary is created using natural language processing techniques. In this paper, we have discussed various text summarization models and presented theresults of our own implementation of automatic text summarizer which was trained using the CNN Daily mail dataset.

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Additional details

Identifiers

DOI
10.35940/ijeat.D4404.13040424
EISSN
2249-8958

Dates

Accepted
2024-04-15
Manuscript received on 24 March 2024 | Revised Manuscript received on 03 April 2023 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024.

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