Analysis and Implemented on Automated Text Summarization using Transformer Model
Shruti J. Sapra (Thakur)1, Avinash S. Kapse2, Mohammad Atique3
1Dr. Shruti J. Sapra (Thakur), Research Scholar, Department of Computer Science and Engineering, Sant Gadge Baba Amravati University, Amravati (M.H), India.
2Dr. Avinash S. Kapse, Associate, Professor and Head, Department of Information Technology, Anuradha College of Engineering, Chikhli, Sant Gadge Baba Amravati University, Amravati (M.H), India.
3Dr. Mohammad Atique, Professor and Head, Department of Computer Science and Engineering Sant Gadge Baba Amravati University, Amravati (M.H), India.
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 | PP: 19-22 | Volume-13 Issue-4, April 2024 | Retrieval Number: 100.1/ijeat.D440413040424 | DOI: 10.35940/ijeat.D4404.13040424
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Abstractive Summarization, Extractive Summarization, Natural Language Processing, Text Summarization, Deep Learning, Transformers
Scope of the Article: Deep Learning