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        <title>Summarizing Legal Rulings: Comparative Experiments</title>
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    <name type="personal">
        <namePart type="given">Diego</namePart>
        <namePart type="family">Feijo</namePart>
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    <name type="personal">
        <namePart type="given">Viviane</namePart>
        <namePart type="family">Moreira</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary. Recent neural network-based approaches can achieve high-quality scores for text summarization. However, these approaches have been used mostly for generating very short abstracts for news articles. Thus, their applicability to the legal domain remains an open issue. In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings. These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text. Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_036</identifier>
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        <url>https://aclanthology.org/R19-1036</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Summarizing Legal Rulings: Comparative Experiments
%A Feijo, Diego
%A Moreira, Viviane
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F feijo-moreira-2019-summarizing
%X In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary. Recent neural network-based approaches can achieve high-quality scores for text summarization. However, these approaches have been used mostly for generating very short abstracts for news articles. Thus, their applicability to the legal domain remains an open issue. In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings. These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text. Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.
%R 10.26615/978-954-452-056-4_036
%U https://aclanthology.org/R19-1036
%U https://doi.org/10.26615/978-954-452-056-4_036
%P 313-322

Markdown (Informal)

[Summarizing Legal Rulings: Comparative Experiments](https://aclanthology.org/R19-1036) (Feijo & Moreira, RANLP 2019)

• Summarizing Legal Rulings: Comparative Experiments (Feijo & Moreira, RANLP 2019)

ACL

• Diego Feijo and Viviane Moreira. 2019. Summarizing Legal Rulings: Comparative Experiments. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 313–322, Varna, Bulgaria. INCOMA Ltd..