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        <namePart type="given">Leonardo</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Legal document summarization aims to provide a clear understanding of the main points and arguments in a legal document, contributing to the efficiency of the judicial system. In this paper, we propose BB25HLegalSum, a method that combines BERT clusters with the BM25 algorithm to summarize legal documents and present them to users with highlighted important information. The process involves selecting unique, relevant sentences from the original document, clustering them to find sentences about a similar subject, combining them to generate a summary according to three strategies, and highlighting them to the user in the original document. We outperformed baseline techniques using the BillSum dataset, a widely used benchmark in legal document summarization. Legal workers positively assessed the highlighted presentation.</abstract>
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%0 Conference Proceedings
%T BB25HLegalSum: Leveraging BM25 and BERT-Based Clustering for the Summarization of Legal Documents
%A de Andrade, Leonardo
%A Becker, Karin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F de-andrade-becker-2023-bb25hlegalsum
%X Legal document summarization aims to provide a clear understanding of the main points and arguments in a legal document, contributing to the efficiency of the judicial system. In this paper, we propose BB25HLegalSum, a method that combines BERT clusters with the BM25 algorithm to summarize legal documents and present them to users with highlighted important information. The process involves selecting unique, relevant sentences from the original document, clustering them to find sentences about a similar subject, combining them to generate a summary according to three strategies, and highlighting them to the user in the original document. We outperformed baseline techniques using the BillSum dataset, a widely used benchmark in legal document summarization. Legal workers positively assessed the highlighted presentation.
%U https://aclanthology.org/2023.ranlp-1.29
%P 255-263

Markdown (Informal)

[BB25HLegalSum: Leveraging BM25 and BERT-Based Clustering for the Summarization of Legal Documents](https://aclanthology.org/2023.ranlp-1.29) (de Andrade & Becker, RANLP 2023)

• BB25HLegalSum: Leveraging BM25 and BERT-Based Clustering for the Summarization of Legal Documents (de Andrade & Becker, RANLP 2023)

ACL

• Leonardo de Andrade and Karin Becker. 2023. BB25HLegalSum: Leveraging BM25 and BERT-Based Clustering for the Summarization of Legal Documents. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 255–263, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.