# Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents

arXiv: https://arxiv.org/abs/2106.15876 · HTML: https://mlsanity.com/paper/2106.15876/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [Extractive Summarization of Long Documents by Combining Global and Local Context](https://mlsanity.com/paper/1909.08089/) (2019)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Multi-document abstractive summarization using ILP based multi-sentence compression](https://mlsanity.com/paper/1609.07034/) (2016)
- [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)

## Built on by (most cited first, 23 of 23)

- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [Fact-based Court Judgment Prediction](https://mlsanity.com/paper/2311.13350/) (2023)
- [Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance](https://mlsanity.com/paper/2210.12437/) (2022)
- [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)
- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland](https://mlsanity.com/paper/2410.13456/) (2024)
- [LawSum: A weakly supervised approach for Indian Legal Document Summarization](https://mlsanity.com/paper/2110.01188/) (2021)
- [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)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization](https://mlsanity.com/paper/2305.11592/) (2023)
- [Citation-Based Summarization of Landmark Judgments](https://mlsanity.com/paper/2406.10824/) (2024)
- [Human Centered AI for Indian Legal Text Analytics](https://mlsanity.com/paper/2403.10944/) (2024)
- [ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data](https://mlsanity.com/paper/2405.06541/) (2024)
- [RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity](https://mlsanity.com/paper/2501.14113/) (2025)
- [Advantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindi](https://mlsanity.com/paper/2602.07382/) (2026)
- [Summarisation of German Judgments in conjunction with a Class-based Evaluation](https://mlsanity.com/paper/2505.05947/) (2025)
- [ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India](https://mlsanity.com/paper/2512.18014/) (2025)
- [Extractive summarization on a CMOS Ising machine](https://mlsanity.com/paper/2601.11491/) (2026)
- [ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification](https://mlsanity.com/paper/2604.22292/) (2026)

## Abstract (arXiv)

Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guidelines from legal experts into an optimization setup. We conduct detailed experiments over case documents from the Indian Supreme Court. The experiments show that our proposed unsupervised method outperforms several strong baselines in terms of ROUGE scores, including both general summarization algorithms and legal-specific ones. In fact, though our proposed algorithm is unsupervised, it outperforms several supervised summarization models that are trained over thousands of document-summary pairs.