mlsanityData to 14 Oct 2026

Argumentative Segmentation Enhancement for Legal Summarization

arXiv 2307.05081 ↗First posted 11 Jul 2023cs.CLcited by 4

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Abstract (arXiv)

We use the combination of argumentative zoning [1] and a legal argumentative scheme to create legal argumentative segments. Based on the argumentative segmentation, we propose a novel task of classifying argumentative segments of legal case decisions. GPT-3.5 is used to generate summaries based on argumentative segments. In terms of automatic evaluation metrics, our method generates higher quality argumentative summaries while leaving out less relevant context as compared to GPT-4 and non-GPT models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

14 of 41 references.

References · 14

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  12. 2019Leveraging BERT for Extractive Text Summarization on Lecturescited by 63
2 more
  1. 2022ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Miningcited by 25
  2. 2022Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisionscited by 8

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  2. 2025Large Language Models in Argument Mining: A Surveycited by 14
  3. 2024LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in Englishcited by 8
  4. 2025A Comprehensive Survey on Legal Summarization: Challenges and Future Directionscited by 6