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

TL;DR

This paper demonstrates that injecting legal domain knowledge into neural summarization models significantly improves the quality and factual accuracy of summaries for Indian court judgments in both English and Hindi.

Contribution

The study introduces a framework for incorporating legal domain knowledge into extractive and generative summarization models, enhancing Indian legal document summarization in multiple languages.

Findings

Significant improvements in summarization quality metrics

Enhanced factual consistency and legal relevance

Validated effectiveness through domain expert evaluations

Abstract

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.