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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.540.pdf
- Code
- engsalem/arglegalsumm