# Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

arXiv: https://arxiv.org/abs/2210.07544 · HTML: https://mlsanity.com/paper/2210.07544/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [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)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [What Have We Achieved on Text Summarization?](https://mlsanity.com/paper/2010.04529/) (2020)
- [Content Selection in Deep Learning Models of Summarization](https://mlsanity.com/paper/1810.12343/) (2018)
- [Plain English Summarization of Contracts](https://mlsanity.com/paper/1906.00424/) (2019)
- [Long Document Summarization in a Low Resource Setting using Pretrained Language Models](https://mlsanity.com/paper/2103.00751/) (2021)
- [On Generating Extended Summaries of Long Documents](https://mlsanity.com/paper/2012.14136/) (2020)
- [A Survey on Neural Network-Based Summarization Methods](https://mlsanity.com/paper/1804.04589/) (2018)
- [A Divide-and-Conquer Approach to the Summarization of Long Documents](https://mlsanity.com/paper/2004.06190/) (2020)

## Built on by (most cited first, 24 of 24)

- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2309.17050/) (2023)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [Low-Resource Court Judgment Summarization for Common Law Systems](https://mlsanity.com/paper/2403.04454/) (2024)
- [MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments](https://mlsanity.com/paper/2310.18600/) (2023)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation](https://mlsanity.com/paper/2508.08730/) (2025)
- [CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization](https://mlsanity.com/paper/2408.06576/) (2024)
- [Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.09623/) (2024)
- [How Private are Language Models in Abstractive Summarization?](https://mlsanity.com/paper/2412.12040/) (2024)
- [Automated Argument Generation from Legal Facts](https://mlsanity.com/paper/2310.05680/) (2023)
- [Human Centered AI for Indian Legal Text Analytics](https://mlsanity.com/paper/2403.10944/) (2024)
- [Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK](https://mlsanity.com/paper/2406.00040/) (2024)
- [CourtPressGER: A German Court Decision to Press Release Summarization Dataset](https://mlsanity.com/paper/2512.09434/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India](https://mlsanity.com/paper/2507.06090/) (2025)
- [LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents](https://mlsanity.com/paper/2509.02241/) (2025)
- [AugAbEx: Bridging Abstractive and Extractive Legal Summarization](https://mlsanity.com/paper/2511.12290/) (2025)
- [Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers](https://mlsanity.com/paper/2607.21010/) (2026)
- [Annotating Topical Legal Insights from Case Proceedings](https://mlsanity.com/paper/2607.27792/) (2026)
- [Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG](https://mlsanity.com/paper/2609.03749/) (2026)
- [SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges](https://mlsanity.com/paper/2610.19992/) (2026)

## Abstract (arXiv)

Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer-based abstractive summarization models have restrictions on the number of input tokens, and legal documents are known to be very long. Also, it is an open question on how best to evaluate legal case document summarization systems. In this paper, we carry out extensive experiments with several extractive and abstractive summarization methods (both supervised and unsupervised) over three legal summarization datasets that we have developed. Our analyses, that includes evaluation by law practitioners, lead to several interesting insights on legal summarization in specific and long document summarization in general.