MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Submission Type: Regular Short Paper
Submission Track: Resources and Evaluation
Submission Track 2: Summarization
Keywords: Cross-Lingual Summarization, Multilingual corpus for Summarization, Summarization-Translation Pipeline, Legal NLP
TL;DR: A Dataset Creation and Benchmarking Study for Multilingual (English and Hindi) Summarization of Legal Case Judgments in India.
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.
Submission Number: 1970
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