Pith. sign in

REVIEW 1 cited by

Methods for Computing Legal Document Similarity: A Comparative Study

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.12307 v1 pith:YCHE2ATV submitted 2020-04-26 cs.SI cs.CLcs.IRcs.LG

Methods for Computing Legal Document Similarity: A Comparative Study

Paheli Bhattacharya , Kripabandhu Ghosh , Arindam Pal , Saptarshi Ghosh This is my paper
classification cs.SI cs.CLcs.IRcs.LG
keywords legalsimilaritymethodsdocumentsbeencomputingexistingmeasuring
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering

    stat.ML 2025-08 reject novelty 4.0

    Concatenating Top2Vec and Node2Vec embeddings, where the Node2Vec graph encodes Top2Vec's own topic labels, yields compact clusters, but the gain is largely circular.