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  Published Paper Details:

  Paper Title

Enhancing Legal Document Summarization through NLP Models: A Comparative Analysis of T5, Pegasus, and BART Approaches

  Authors

  Ifat Jagirdar,  Sakshi Gandage,  Bhakti Waghmare,  Iffat Kazi

  Keywords

Index Terms--Legal documents,BART,Pegasus,T5,summarize

  Abstract


Using cutting-edge language models like T5, BART, and Pegasus, this study tackles the pressing need for a sophisticated legal document summarizer. The importance of our work stems from the growing amount and complexity of legal documents, which calls for effective summarising methods to expedite information retrieval and decision-making in the legal field Our research concentrated on integrating the T5, BART, and Pegasus models to improve the legal document processing system's summarising capabilities. The significance of this study arises from the inherent difficulties in fully comprehending and retrieving pertinent data from lengthy legal texts, which are frequently dense with complex legal jargon and minute minutiae. As we move forward, our work has established a strong framework for summarising legal documents, giving legal experts an effective tool to speed up the evaluation and interpretation of legal content. Using T5, BART, and Pegasus to their full potential, our solution enhances legal document processing productivity while also adding to the growing field of legal natural language processing applications. In summary, this study fills a critical gap in the legal community by highlighting the significance of sophisticated summarising methods for handling the everincreasing amount of legal documentation. Our work not only highlights the importance of utilising state-of-the-art language models, but also offers a workable method that advances legal document summarising into a more advanced and effective domain.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A3273

  Paper ID - 254637

  Page Number(s) - k805-k817

  Pubished in - Volume 12 | Issue 3 | March 2024

  DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.38685

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ifat Jagirdar,  Sakshi Gandage,  Bhakti Waghmare,  Iffat Kazi,   "Enhancing Legal Document Summarization through NLP Models: A Comparative Analysis of T5, Pegasus, and BART Approaches", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 3, pp.k805-k817, March 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A3273.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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