Legal document summarization: a short review
Abstract
This mini review surveys published work on automatic summarization of legal judgments. We focus on how natural language processing handles argument structure, discourse, and the role of citations and precedent when systems generate shorter versions of a case. The field began with extractive methods and classical machine learning, and moved through graph-based and neural models, and to recent transformer architectures and large language models (LLMs). Some of the recent papers pair LLMs with retrieval augmentation so that outputs are faithful to the source text. A scheme is repeated in the domain that whether researchers should work at sentence or clause level, how rhetorical roles are defined, and how to judge a summary when fluent wording can still misrepresent the judgment. Many limitations still exist where many studies rely on general-domain setups or small, jurisdiction-specific corpora; citations are often treated as meta-data rather than part of the argument; and evaluation still depends heavily on overlap with reference summaries, which only partly reflects legal quality. It is also non-practical to compare results when datasets and benchmarks differ from one country to another. We situate this review next to wider surveys of legal summarization and with LLMs in law, but with only the judgment documents. Finally, we guide in future directions with more languages and jurisdictions, summaries that users can trace back to the judgment, and evaluation that mixes…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.