A Survey on AI-Powered Legal Document Summarization

The legal domain is characterized by an overwhelming volume of complex, high-stakes documentation, where manual analysis often results in significant cognitive load and potential for human error. While conventional Natural Language Processing (NLP) tools have made strides in general text processing, they frequently struggle with the specialized vocabulary and rhetorical nuances inherent in legal discourse. This paper presents a systematic survey of AI-powered legal document summarization techniques using transformer-based NLP models. Existing legal summarization approaches commonly utilize transformer-based models along with Named Entity Recognition (NER) and Rhetorical Role Labeling (RRL). Furthermore, this survey identifies critical research gaps within the Indian legal context, including document truncation issues, the need for multilingual support, and the demand for explainable AI (XAI) to foster trust among legal professionals. By synthesizing current research trends and experimental findings, this work provides a comprehensive roadmap for the development of intelligent, scalable solutions that enhance information accessibility and productivity in the legal sector.