A Comprehensive Survey on Legal Summarization: Challenges and Future Directions

Mousumi Akter, Erion Çano, Erik Weber, Dennis Dobler, Ivan Habernal
1/29/2025
cs.CL

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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.

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Cite this paper

@article{akter2025comprehensive,
  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},
  year   = {2025},
  eprint = {2501.17830},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.17830v1}
}

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