ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

Mohamed Elaraby; Diane Litman

ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

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Abstract

A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines.

Anthology ID:: 2022.coling-1.540
Volume:: Proceedings of the 29th International Conference on Computational Linguistics
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:: COLING
SIG:
Publisher:: International Committee on Computational Linguistics
Note:
Pages:: 6187–6194
Language:
URL:: https://aclanthology.org/2022.coling-1.540/
DOI:
Bibkey:
Cite (ACL):: Mohamed Elaraby and Diane Litman. 2022. ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6187–6194, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining (Elaraby & Litman, COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.540.pdf

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@inproceedings{elaraby-litman-2022-arglegalsumm,
    title = "{A}rg{L}egal{S}umm: Improving Abstractive Summarization of Legal Documents with Argument Mining",
    author = "Elaraby, Mohamed  and
      Litman, Diane",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.540/",
    pages = "6187--6194",
    abstract = "A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines."
}

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%0 Conference Proceedings
%T ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
%A Elaraby, Mohamed
%A Litman, Diane
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F elaraby-litman-2022-arglegalsumm
%X A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines.
%U https://aclanthology.org/2022.coling-1.540/
%P 6187-6194

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Markdown (Informal)

[ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://aclanthology.org/2022.coling-1.540/) (Elaraby & Litman, COLING 2022)

ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining (Elaraby & Litman, COLING 2022)

ACL

Mohamed Elaraby and Diane Litman. 2022. ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6187–6194, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.