# Methods for Computing Legal Document Similarity: A Comparative Study

arXiv: https://arxiv.org/abs/2004.12307 · HTML: https://mlsanity.com/paper/2004.12307/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

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

- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [Evaluating Document Representations for Content-based Legal Literature Recommendations](https://mlsanity.com/paper/2104.13841/) (2021)
- [Rethinking Word Similarity: Semantic Similarity through Classification Confusion](https://mlsanity.com/paper/2502.05704/) (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)
- [Citation Recommendation on Scholarly Legal Articles](https://mlsanity.com/paper/2311.05902/) (2023)
- [Judgment2vec: Apply Graph Analytics to Searching and Recommendation of Similar Judgments](https://mlsanity.com/paper/2408.04382/) (2024)
- [How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching](https://mlsanity.com/paper/2502.18292/) (2025)

## Abstract (arXiv)

Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval. Finding similar legal documents has many applications in downstream tasks, including prior-case retrieval, recommendation of legal articles, and so on. Prior works have proposed two broad ways of measuring similarity between legal documents - analyzing the precedent citation network, and measuring similarity based on textual content similarity measures. But there has not been a comprehensive comparison of these existing methods on a common platform. In this paper, we perform the first systematic analysis of the existing methods. In addition, we explore two promising new similarity computation methods - one text-based and the other based on network embeddings, which have not been considered till now.