# Legal Case Document Similarity: You Need Both Network and Text

arXiv: https://arxiv.org/abs/2209.12474 · HTML: https://mlsanity.com/paper/2209.12474/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2022.

## Built on by (most cited first, 16 of 16)

- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023)
- [CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding](https://mlsanity.com/paper/2305.05393/) (2023)
- [DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment](https://mlsanity.com/paper/2403.18435/) (2024)
- [LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases](https://mlsanity.com/paper/2501.14114/) (2025)
- [MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents](https://mlsanity.com/paper/2503.10659/) (2025)
- [Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models](https://mlsanity.com/paper/2507.02182/) (2025)
- [Explainable identification of similarities between entities for discovery in large text](https://mlsanity.com/paper/2503.17605/) (2025)
- [Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering](https://mlsanity.com/paper/2509.00990/) (2025)
- [Analysing the Resourcefulness of the Paragraph for Precedence Retrieval](https://mlsanity.com/paper/2308.01203/) (2023)
- [Enhancing Criminal Case Matching through Diverse Legal Factors](https://mlsanity.com/paper/2406.11172/) (2024)
- [Empirical analysis of binding precedent efficiency in Brazilian Supreme Court via case classification](https://mlsanity.com/paper/2407.07004/) (2024)
- [A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction](https://mlsanity.com/paper/2408.02233/) (2024)
- [Incorporating Legal Logic into Deep Learning: An Intelligent Approach to Probation Prediction](https://mlsanity.com/paper/2508.12286/) (2025)
- [A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification](https://mlsanity.com/paper/2603.09990/) (2026)
- [PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation](https://mlsanity.com/paper/2607.09094/) (2026)

## Abstract (arXiv)

Estimating the similarity between two legal case documents is an important and challenging problem, having various downstream applications such as prior-case retrieval and citation recommendation. There are two broad approaches for the task -- citation network-based and text-based. Prior citation network-based approaches consider citations only to prior-cases (also called precedents) (PCNet). This approach misses important signals inherent in Statutes (written laws of a jurisdiction). In this work, we propose Hier-SPCNet that augments PCNet with a heterogeneous network of Statutes. We incorporate domain knowledge for legal document similarity into Hier-SPCNet, thereby obtaining state-of-the-art results for network-based legal document similarity. Both textual and network similarity provide important signals for legal case similarity; but till now, only trivial attempts have been made to unify the two signals. In this work, we apply several methods for combining textual and network information for estimating legal case similarity. We perform extensive experiments over legal case documents from the Indian judiciary, where the gold standard similarity between document-pairs is judged by law experts from two reputed Law institutes in India. Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts' opinion when compared to the existing methods for network-based legal document similarity. Our best-performing combination method (that combines network-based and text-based similarity) improves the correlation with domain experts' opinion by 11.8% over the best text-based method and 20.6% over the best network-based method. We also establish that our best-performing method can be used to recommend / retrieve citable and similar cases for a source (query) case, which are well appreciated by legal experts.