# Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation

arXiv: https://arxiv.org/abs/2504.20323 · HTML: https://mlsanity.com/paper/2504.20323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [MUSER: A Multi-View Similar Case Retrieval Dataset](https://mlsanity.com/paper/2310.15602/) (2023)
- [Evaluating Document Representations for Content-based Legal Literature Recommendations](https://mlsanity.com/paper/2104.13841/) (2021)
- [An empirical evaluation of using ChatGPT to summarize disputes for recommending similar labor and employment cases in Chinese](https://mlsanity.com/paper/2409.09280/) (2024)

## Abstract (arXiv)

This report addresses the challenge of limited labeled datasets for developing legal recommender systems, particularly in specialized domains like labor disputes. We propose a new approach leveraging the co-citation of legal articles within cases to establish similarity and enable algorithmic annotation. This method draws a parallel to the concept of case co-citation, utilizing cited precedents as indicators of shared legal issues. To evaluate the labeled results, we employ a system that recommends similar cases based on plaintiffs' accusations, defendants' rebuttals, and points of disputes. The evaluation demonstrates that the recommender, with finetuned text embedding models and a reasonable BiLSTM module can recommend labor cases whose similarity was measured by the co-citation of the legal articles. This research contributes to the development of automated annotation techniques for legal documents, particularly in areas with limited access to comprehensive legal databases.