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.
@article{arxiv.2501.17830,
title = {A Comprehensive Survey on Legal Summarization: Challenges and Future Directions},
author = {Mousumi Akter and Erion Çano and Erik Weber and Dennis Dobler and Ivan Habernal},
journal= {arXiv preprint arXiv:2501.17830},
year = {2025}
}