MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 28 Oct 2023.
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24 of 34 references.
References · 24
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Citing papers per year: 2024: 4; 2025: 4; 2026: 2.
10 papers.
- 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontierscited by 51
- 2024Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerlandcited by 7
- 2025A Comprehensive Survey on Legal Summarization: Challenges and Future Directionscited by 6
- 2026Do LLM hallucination detectors suffer from low-resource effect?cited by 4
- 2024The State and Fate of Summarization Datasets: A Surveycited by 3
- 2024Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5cited by 2
- 2025Computational Law: Datasets, Benchmarks, and Ontologiescited by 2
- 2026Advantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindicited by 1
- 2025Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languagescited by 0
- 2025ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in Indiacited by 0
- 2026Advantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindi7 Feb 2026
- 2026Do LLM hallucination detectors suffer from low-resource effect?23 Jan 2026
- 2025ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India19 Dec 2025
- 2025Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages8 Oct 2025
- 2025Computational Law: Datasets, Benchmarks, and Ontologies6 Mar 2025
- 2025A Comprehensive Survey on Legal Summarization: Challenges and Future Directions29 Jan 2025
- 2024The State and Fate of Summarization Datasets: A Survey7 Nov 2024
- 2024Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland17 Oct 2024
- 2024Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E59 Sep 2024
- 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers7 Apr 2024