MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments

Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, Saptarshi Ghosh


Abstract
Automatic summarization of legal case judgments is a practically important problem that has attracted substantial research efforts in many countries. In the context of the Indian judiciary, there is an additional complexity – Indian legal case judgments are mostly written in complex English, but a significant portion of India’s population lacks command of the English language. Hence, it is crucial to summarize the legal documents in Indian languages to ensure equitable access to justice. While prior research primarily focuses on summarizing legal case judgments in their source languages, this study presents a pioneering effort toward cross-lingual summarization of English legal documents into Hindi, the most frequently spoken Indian language. We construct the first high-quality legal corpus comprising of 3,122 case judgments from prominent Indian courts in English, along with their summaries in both English and Hindi, drafted by legal practitioners. We benchmark the performance of several diverse summarization approaches on our corpus and demonstrate the need for further research in cross-lingual summarization in the legal domain.
Anthology ID:
2023.emnlp-main.321
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5291–5302
Language:
URL:
https://aclanthology.org/2023.emnlp-main.321
DOI:
10.18653/v1/2023.emnlp-main.321
Bibkey:
Cite (ACL):
Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, and Saptarshi Ghosh. 2023. MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5291–5302, Singapore. Association for Computational Linguistics.
Cite (Informal):
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments (Datta et al., EMNLP 2023)
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