mlsanityData to 13 Oct 2026

Advantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindi

arXiv 2602.07382 ↗First posted 7 Feb 2026cs.CL cs.AIcited by 1

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Abstract (arXiv)

Summarizing Indian legal court judgments is a complex task not only due to the intricate language and unstructured nature of the legal texts, but also since a large section of the Indian population does not understand the complex English in which legal text is written, thus requiring summaries in Indian languages. In this study, we aim to improve the summarization of Indian legal text to generate summaries in both English and Hindi (the most widely spoken Indian language), by injecting domain knowledge into diverse summarization models. We propose a framework to enhance extractive neural summarization models by incorporating domain-specific pre-trained encoders tailored for legal texts. Further, we explore the injection of legal domain knowledge into generative models (including Large Language Models) through continual pre-training on large legal corpora in English and Hindi. Our proposed approaches achieve statistically significant improvements in both English-to-English and English-to-Hindi Indian legal document summarization, as measured by standard evaluation metrics, factual consistency metrics, and legal domain-specific metrics. Furthermore, these improvements are validated through domain experts, demonstrating the effectiveness of our approaches.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 7 Feb 2026.

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18 of 27 references.

References · 18

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,549
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,231
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,480
  4. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,815
  5. 2020Language-agnostic BERT Sentence Embeddingcited by 622
  6. 2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projectioncited by 456
  7. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 404
  8. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
  9. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 293
  10. 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 245
  11. 2021CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairscited by 39
  12. 2022Pre-trained Language Models for the Legal Domain: A Case Study on Indian Lawcited by 39
6 more
  1. 2022SKILL: Structured Knowledge Infusion for Large Language Modelscited by 27
  2. 2021Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documentscited by 23
  3. 2024Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarizationcited by 23
  4. 2023KILM: Knowledge Injection into Encoder-Decoder Language Modelscited by 19
  5. 2022HLDC: Hindi Legal Documents Corpuscited by 11
  6. 2023MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgmentscited by 10

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Citing papers per year: 2026: 1.

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  1. 2026Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classificationcited by 0