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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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.540.pdf
- Code
- engsalem/arglegalsumm
Export citation
@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
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.