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        <namePart type="given">Valentin</namePart>
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    <abstract>Legal documents are notorious for their length and complexity, making it challenging to extract crucial information efficiently. In this paper, we introduce a new dataset for legal text summarization, covering 24 languages. We not only present and analyze the dataset but also conduct experiments using various extractive techniques. We provide a comparison between these techniques and summaries generated by the state-of-the-art GPT models. The abstractive GPT approach outperforms the extractive TextRank approach in 8 languages, but produces slightly lower results in the remaining 16 languages. This research aims to advance the field of legal document summarization by addressing the need for accessible and comprehensive information retrieval from lengthy legal texts.</abstract>
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%0 Conference Proceedings
%T EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation
%A Zmiycharov, Valentin
%A Tsonkov, Todor
%A Koychev, Ivan
%S Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)
%D 2024
%8 September
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F zmiycharov-etal-2024-eurlexsummarization
%X Legal documents are notorious for their length and complexity, making it challenging to extract crucial information efficiently. In this paper, we introduce a new dataset for legal text summarization, covering 24 languages. We not only present and analyze the dataset but also conduct experiments using various extractive techniques. We provide a comparison between these techniques and summaries generated by the state-of-the-art GPT models. The abstractive GPT approach outperforms the extractive TextRank approach in 8 languages, but produces slightly lower results in the remaining 16 languages. This research aims to advance the field of legal document summarization by addressing the need for accessible and comprehensive information retrieval from lengthy legal texts.
%U https://aclanthology.org/2024.clib-1.22
%P 206-213

Markdown (Informal)

[EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation](https://aclanthology.org/2024.clib-1.22) (Zmiycharov et al., CLIB 2024)

• EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation (Zmiycharov et al., CLIB 2024)

ACL

• Valentin Zmiycharov, Todor Tsonkov, and Ivan Koychev. 2024. EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation. In Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024), pages 206–213, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.