# Argumentative Segmentation Enhancement for Legal Summarization

arXiv: https://arxiv.org/abs/2307.05081 · HTML: https://mlsanity.com/paper/2307.05081/

## 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 (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions](https://mlsanity.com/paper/2211.03229/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)

## 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.