# Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance

arXiv: https://arxiv.org/abs/2210.12437 · HTML: https://mlsanity.com/paper/2210.12437/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## 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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Variations of the Similarity Function of TextRank for Automated Summarization](https://mlsanity.com/paper/1602.03606/) (2016)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Systematically Exploring Redundancy Reduction in Summarizing Long Documents](https://mlsanity.com/paper/2012.00052/) (2020)
- [Context-Aware Legal Citation Recommendation using Deep Learning](https://mlsanity.com/paper/2106.10776/) (2021)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification](https://mlsanity.com/paper/1810.07513/) (2018)

## Built on by (most cited first, 9 of 9)

- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [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)
- [Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization](https://mlsanity.com/paper/2403.19317/) (2024)
- [Better RAG using Relevant Information Gain](https://mlsanity.com/paper/2407.12101/) (2024)
- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [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)
- [LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation](https://mlsanity.com/paper/2605.19815/) (2026)

## Abstract (arXiv)

Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of legal decisions in a low-resource setting using limited expert annotated data. We test a set of models that locate relevant content using a sequential model and tackle redundancy by leveraging maximal marginal relevance to compose summaries. We also demonstrate an implicit approach to help train our proposed models generate more informative summaries. Our multi-task learning model variant leverages rhetorical role identification as an auxiliary task to further improve the summarizer. We perform extensive experiments on datasets containing legal decisions from the US Board of Veterans' Appeals and conduct quantitative and expert-ranked evaluations of our models. Our results show that the proposed approaches can achieve ROUGE scores vis-`a-vis expert extracted summaries that match those achieved by inter-annotator comparison.