MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
October 28, 2023 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
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Authors
Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, Saptarshi Ghosh
arXiv ID
2310.18600
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
18
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
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.
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