A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
This is an incremental work that organizes and reviews existing research for researchers in legal NLP.
The paper provides a systematic survey of over 120 papers on automatic summarization techniques, datasets, models, and evaluation methods in the legal domain, addressing a gap in existing surveys.
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.