# Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization

arXiv: https://arxiv.org/abs/2403.19317 · HTML: https://mlsanity.com/paper/2403.19317/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 66 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://mlsanity.com/paper/2112.07577/) (2021)
- [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://mlsanity.com/paper/1905.13453/) (2019)
- [Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study](https://mlsanity.com/paper/2001.03844/) (2020)
- [Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization](https://mlsanity.com/paper/2209.03549/) (2022)
- [Adversarial Domain Adaptation for Duplicate Question Detection](https://mlsanity.com/paper/1809.02255/) (2018)
- [Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations](https://mlsanity.com/paper/2110.07581/) (2021)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [Long Document Summarization in a Low Resource Setting using Pretrained Language Models](https://mlsanity.com/paper/2103.00751/) (2021)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Adversarial Domain Adaptation for Machine Reading Comprehension](https://mlsanity.com/paper/1908.09209/) (2019)
- [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://mlsanity.com/paper/2210.09345/) (2022)
- [COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning](https://mlsanity.com/paper/2210.15212/) (2022)
- [Cross-Domain Generalization of Neural Constituency Parsers](https://mlsanity.com/paper/1907.04347/) (2019)
- [CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems](https://mlsanity.com/paper/2010.05139/) (2020)
- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance](https://mlsanity.com/paper/2210.12437/) (2022)

## Built on by (most cited first, 8 of 8)

- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [Compressing Language Models for Specialized Domains](https://mlsanity.com/paper/2502.18424/) (2025)
- [LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law](https://mlsanity.com/paper/2503.03266/) (2025)
- [RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity](https://mlsanity.com/paper/2501.14113/) (2025)
- [CourtPressGER: A German Court Decision to Press Release Summarization Dataset](https://mlsanity.com/paper/2512.09434/) (2025)
- [Are manual annotations necessary for statutory interpretations retrieval?](https://mlsanity.com/paper/2506.13965/) (2025)
- [AugAbEx: Bridging Abstractive and Extractive Legal Summarization](https://mlsanity.com/paper/2511.12290/) (2025)

## Abstract (arXiv)

Legal professionals face the challenge of managing an overwhelming volume of lengthy judgments, making automated legal case summarization crucial. However, prior approaches mainly focused on training and evaluating these models within the same jurisdiction. In this study, we explore the cross-jurisdictional generalizability of legal case summarization models.Specifically, we explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. In particular, we investigate whether supplementing models with unlabeled target jurisdiction corpus and extractive silver summaries obtained from unsupervised algorithms on target data enhances transfer performance. Our comprehensive study on three datasets from different jurisdictions highlights the role of pre-training in improving transfer performance. We shed light on the pivotal influence of jurisdictional similarity in selecting optimal source datasets for effective transfer. Furthermore, our findings underscore that incorporating unlabeled target data yields improvements in general pre-trained models, with additional gains when silver summaries are introduced. This augmentation is especially valuable when dealing with extractive datasets and scenarios featuring limited alignment between source and target jurisdictions. Our study provides key insights for developing adaptable legal case summarization systems, transcending jurisdictional boundaries.