Context-Aware Legal Summarization using Reinforcement Learning

Legal judgments are often lengthy and complex, making it difficult for legal professionals to efficiently extract relevant information such as legal principles, precedents, and case outcomes. While existing automatic summarization techniques have proven useful for general domains, they struggle to address the specific requirements of legal texts. This paper presents a novel context-aware summarization model fine-tuned using reinforcement learning (RL), designed to optimize summaries based on legal-specific criteria. By leveraging a custom reward function, our model enhances the extraction of essential components of judgments, such as legal principles and precedents, while maintaining clarity and coherence. We train and evaluate our model on a dataset of Indian Supreme Court judgments, demonstrating significant improvements in summary quality over baseline models. Our experiments show that RL fine-tuning leads to better alignment with legal requirements, as evidenced by both automatic metrics (ROUGE, BLEU) and domain-specific evaluations. This work offers a step forward in the application of AI to legal text processing, with potential implications for improving the efficiency of legal research and judgment analysis.