# ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

arXiv: https://arxiv.org/abs/2209.01650 · HTML: https://mlsanity.com/paper/2209.01650/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 4 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis](https://mlsanity.com/paper/2010.05369/) (2020)
- [Long Document Summarization in a Low Resource Setting using Pretrained Language Models](https://mlsanity.com/paper/2103.00751/) (2021)
- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [A Bag of Tricks for Dialogue Summarization](https://mlsanity.com/paper/2109.08232/) (2021)

## Built on by (most cited first, 25 of 25)

- [Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization](https://mlsanity.com/paper/2305.16784/) (2023)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking](https://mlsanity.com/paper/2306.00672/) (2023)
- [LexAbSumm: Aspect-based Summarization of Legal Decisions](https://mlsanity.com/paper/2404.00594/) (2024)
- [Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions](https://mlsanity.com/paper/2211.03229/) (2022)
- [Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization](https://mlsanity.com/paper/2403.19317/) (2024)
- [OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset](https://mlsanity.com/paper/2406.14657/) (2024)
- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization](https://mlsanity.com/paper/2502.06185/) (2025)
- [Multi-granularity Argument Mining in Legal Texts](https://mlsanity.com/paper/2210.09472/) (2022)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://mlsanity.com/paper/2212.09825/) (2022)
- [Argumentative Segmentation Enhancement for Legal Summarization](https://mlsanity.com/paper/2307.05081/) (2023)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [STRONG -- Structure Controllable Legal Opinion Summary Generation](https://mlsanity.com/paper/2309.17280/) (2023)
- [ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs](https://mlsanity.com/paper/2505.23654/) (2025)
- [Towards the Exploitation of LLM-based Chatbot for Providing Legal Support to Palestinian Cooperatives](https://mlsanity.com/paper/2306.05827/) (2023)
- [Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law](https://mlsanity.com/paper/2308.11531/) (2023)
- [ReflectSumm: A Benchmark for Course Reflection Summarization](https://mlsanity.com/paper/2403.19012/) (2024)
- [PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization](https://mlsanity.com/paper/2412.12588/) (2024)
- [RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity](https://mlsanity.com/paper/2501.14113/) (2025)
- [LegalViz: Legal Text Visualization by Text To Diagram Generation](https://mlsanity.com/paper/2502.06147/) (2025)
- [Argument Mining as a Text-to-Text Generation Task](https://mlsanity.com/paper/2603.23949/) (2026)
- [Gavel: Agent Meets Checklist for Evaluating LLMs on Long-Context Legal Summarization](https://mlsanity.com/paper/2601.04424/) (2026)
- [Improving Argument Saliency Coverage in Small LLMs for Long Legal Opinion Summarization via Sequence-Level Distillation](https://mlsanity.com/paper/2608.29884/) (2026)

## Abstract (arXiv)

A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines