mlsanityData to 7 Oct 2026

A Comprehensive Survey on Legal Summarization: Challenges and Future Directions

arXiv 2501.17830 ↗First posted 29 Jan 2025cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 55 of its 149 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

55 of 149 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.