Text Summarization Using Machine Learning Algorithm

Authors

  • Dr. Vidyagouri B H  Assistant Professor, Dept of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India1
  • BibiSadiqa M D  Mtech Student Dept of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India

DOI:

https://doi.org//10.32628/CSEIT228421

Keywords:

Text Summarization, Natural Language Processing, Extractive Summarization, Abstractive Summarization, ROUGE.

Abstract

In the age of technology, data is critical. The data on the internet is formless and poorly organized. The concept of text summarization is introduced in order to convert data summaries. Text summarization is the process of extracting useful information from raw data without diluting the main theme of the data. Today’s readers must contend with task of reading comments, reviews, news articles, blogs and other forms of informal and noisy communication. It is difficult to retrieve the correct gist of the gist, which is required by all readers. To achieve the benefits of both extractive and abstractive summarization, the proposed approach combines TF-TDF-TR(Term Frequency – Inverse Document Frequency – Text Rank) as an unsupervised learning algorithm and the seq2seq (Sequence to Sequence) model as a supervised learning algorithm. In terms of ROUGE score, the proposed TFRSP approach outperforms existing text summarization methods, resulting in high summary accuracy.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-08-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Vidyagouri B H, BibiSadiqa M D, " Text Summarization Using Machine Learning Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.167-173, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT228421