ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

Mohamed Elaraby, Diane Litman


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
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
Code
 engsalem/arglegalsumm