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Fuzzy Bi-GRU Based Hybrid Extractive and Abstractive Text Summarization for Long Multi-documents

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Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (SoCPaR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 648))

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Abstract

As a massive amount of information is produced on the internet nowadays, the need for extracting the most useful and relevant information from that massive list is one of the most attractive research and it can be possible through a mechanism called automatic text summarization (ATS). This summarization mechanism is classified into single and multi-documents based on the number of source documents. When multiple source documents communicate similar information called multi-documents and it is the biggest challenge in the field of ATS. This motivates us to work on the long multi-documents by calculating the sentence scores using a fuzzy inference system. From the extracted sentences, the similarity or redundancy has to be removed using Bi- GRU, and then an abstractive summary need to be generated for those identified sentences has to produce. The proposed system is validated and tested using Standard datasets namely, DUC, BBC news, and CNN/daily mail. The proposed Fuzzy Bi-GRU is compared with other cutting-edge models, and empirical results indicate that it outperforms all other models in terms of ROUGE- N and L scores.

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Author information

Authors and Affiliations

  1. GIET University, Gunupur, Odisha, 765022, India

    Gitanjali Mishra & Nilambar Sethi

  2. School of Information Technology and Engineering, VIT Vellore, Vellore, TN, 632014, India

    L. Agilandeeswari

Authors
  1. Gitanjali Mishra
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  2. Nilambar Sethi
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  3. L. Agilandeeswari
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Corresponding authors

Correspondence to Gitanjali Mishra or L. Agilandeeswari .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

    Thomas Hanne

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, AL, USA

    Niketa Gandhi

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Mala, Kerala, India

    Pooja Manghirmalani Mishra

  5. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  6. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Mishra, G., Sethi, N., Agilandeeswari, L. (2023). Fuzzy Bi-GRU Based Hybrid Extractive and Abstractive Text Summarization for Long Multi-documents. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_16

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