MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
This addresses the problem of equitable access to legal information for non-English speakers in India, but it is incremental as it builds on prior summarization work by focusing on cross-lingual aspects.
The study tackled the problem of summarizing English legal case judgments into Hindi to improve access to justice in India, resulting in the creation of a benchmark dataset with 3,122 cases and summaries in both languages, and benchmarking showed the need for further research in cross-lingual summarization.
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.