Legal lay summarization: exploring methods and data generation with large language models

Abstract This paper explores advancements in Natural Language Processing (NLP) for legal lay summarization by systematically analyzing existing methodologies, datasets, and research findings. We review current literature, highlighting key challenges such as data scarcity and the complexity of legal language. A primary contribution of this study is the development of LegalEase, a specialized dataset designed to improve model training for summarizing legal documents in layman’s terms. Our findings demonstrate that subdomain-specific datasets within the legal domain outperform general legal datasets in enhancing NLP model performance for generating accurate and comprehensible legal summaries. The insights and methodologies presented provide a foundation for future research in legal lay summarization.