A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
🤖 AI Summary
This paper addresses the absence of a systematic survey of legal text summarization research in the Transformer era. It presents the first rigorous, comprehensive review covering over 120 scholarly works. Methodologically, it integrates bibliometric analysis, cross-model comparative evaluation, and a novel “task–data–evaluation” three-dimensional mapping framework to construct the first end-to-end research landscape for legal summarization. Key contributions include: (1) a multidimensional taxonomy that identifies three persistent challenges—modeling long-range dependencies, poor domain adaptability, and inconsistent evaluation criteria; (2) a structured knowledge base synthesizing empirical findings across models, datasets, and metrics; and (3) actionable recommendations for model optimization and benchmark development. Collectively, this work provides both theoretical foundations and practical guidance for advancing legal NLP summarization research.
Application Category
📝 Abstract
This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus filling a gap in existing systematic surveys on the matter. We present existing research along several axes and discuss trends, challenges, and opportunities for future research.
Problem
Research questions and friction points this paper is trying to address.
Legal Document Summarization
Natural Language Processing
Transformer Techniques
Innovation
Methods, ideas, or system contributions that make the work stand out.
Transformer Era
Automated Legal Document Summarization
Comprehensive Literature Review
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