PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
Quick Answer
This paper shows that The PRecG pipeline enhances legal precedent retrieval by utilizing graph neural networks and rhetorical role segmentation, improving semantic similarity computation between legal judgments.
Quick Take
Extensive experiments on an Indian legal dataset show its effectiveness over state-of-the-art methods.
Key Points
- PRecG decomposes legal documents into semantic segments based on rhetorical roles.
- Knowledge graphs capture legal entities and relationships within each segment.
- Segment-level embeddings are aggregated to form a unified document representation.
- The approach outperforms existing methods on a benchmark Indian legal dataset.
- PRecG addresses nuanced legal meanings often overlooked by traditional retrieval methods.
Paper Resources
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~2 min readAbstract:Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic precedent retrieval map legal documents to a low-dimensional semantic space and compute similarity based on the proximity of their representations. These approaches treat legal documents as monolithic texts, ignoring the rhetorical organization of the legal technicalities. Ergo, they overlook nuanced legal meanings and fail to distinguish the contextual significance of legal entities and concepts that vary based on their rhetorical roles within the document.
To address this insufficiency, we propose the PRecG pipeline that computes the similarity between pairs of legal judgments by hierarchically learning their representations. The process begins by decomposing each document into distinct semantic units (segments) based on the rhetorical roles of sentences. For each rhetorical segment, a knowledge graph is constructed to capture the legal entities and their relationships within the segment. Contextual representations of the entities are then learned and aggregated to derive segment-level embeddings. These embeddings are further integrated to produce a unified document-level representation, and finally, the semantic similarity between a pair of documents is computed. We validate the performance of the proposed approach through extensive experiments on a benchmark Indian legal dataset, comparing it against state-of-the-art baselines to demonstrate its effectiveness.
| Comments: | 23 Pages |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.09094 [cs.CL] |
| (or arXiv:2607.09094v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09094 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vikas Kumar [view email]
[v1]
Fri, 10 Jul 2026 04:35:04 UTC (1,095 KB)
— Originally published at arxiv.org
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