Summarization of Legal Texts: A Review of Approaches, Challenges, and Applications in Judicial Analysis

Abstract

The rapid expansion of digital legal information, including court judgments, regulatory notices, and compliance documents, has increased the need for automatic summarization techniques in legal technology. Legal text summarization focuses on condensing extensive, domain-specific materials into concise, coherent, and legally accurate representations to assist judges, lawyers, and regulatory professionals in making faster and more informed decisions. However, the nature of legal writing, which involves specialized terminology, interconnected references, and context-dependent phrasing, presents challenges that are not commonly encountered in general-domain summarization. This review examines major advancements in legal text summarization research published between 2019 and 2025, covering neural extractive methods, abstractive architectures, and hybrid frameworks that incorporate domain knowledge. Evaluation metrics such as ROUGE, BLEU, and BERTScore are discussed together with benchmark datasets developed across multiple jurisdictions. The study highlights key limitations, including the scarcity of annotated legal datasets, multilingual challenges, and concerns related to factual consistency in transformer-based models. It also discusses applications of summarization in improving accessibility of legal information and supporting judicial analysis workflows. The review concludes by emphasizing the importance of transparent, domain-adapted, and reliable summarization systems that achieve an effective balance between precision, interpretability, and efficiency.