EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AEPJJHCW5" target="_blank" >RIV/00216208:11320/25:EPJJHCW5 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.clib-1.22" target="_blank" >https://aclanthology.org/2024.clib-1.22</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

EurLexSummarization – A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)

ISBN

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ISSN

2367-5578

e-ISSN

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Počet stran výsledku

8

Strana od-do

206-213

Název nakladatele

Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences

Místo vydání

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Místo konání akce

Sofia, Bulgaria

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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