A NOVEL APPROACH TO LEGAL TEXT SUMMARIZATION WITH DEEP REINFORCEMENT LEARNING

This paper presents a novel approach to legal text summarization utilizing Deep Reinforcement Learning (DRL) to enhance the efficiency and accuracy of summarizing complex legal documents. Traditional summarization techniques, including extractive and abstractive methods, often struggle to capture the nuances and intricacies of legal language, leading to summaries that may lack coherence or precision. The proposed DRL framework addresses these limitations by employing a policy network that dynamically selects relevant sentences and phrases from legal texts, guided by a reward function that evaluates summary quality based on accuracy, relevance, and coherence. Experimental results demonstrate that the DRL framework significantly outperforms existing summarization methods, providing legal professionals with concise, context-aware summaries that facilitate better decision-making and communication. The findings suggest that this innovative approach not only improves the quality of legal text summarization but also has broader implications for enhancing access to legal information and supporting informed public engagement with legal issues.