梳理120+篇法律文本摘要研究，揭示领域挑战与未来方向
A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
- 系统综述120+篇法律摘要论文，覆盖Transformer时代
- 归纳现有方法、数据集、模型与评估体系
- 适合法律AI、NLP研究者参考
本文系统综述了自然语言处理（NLP）中现代Transformer时代以来的法律文本自动摘要技术，涵盖研究方法、数据集、模型与评估手段。基于严格筛选标准，共分析超过120篇相关论文，填补了现有系统性综述的空白。文章从多个维度梳理了当前研究进展，探讨了发展趋势、现存挑战与未来研究机遇。
原文摘要 · Abstract (English)
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.
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