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PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation

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PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
#precg#legal#precedent#retrieval#graph
TL;DR · WeSearch summary

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.

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Original publisherarXiv cs.AI
Canonical URLhttps://arxiv.org/abs/2607.09094
Publication timeMon, 13 Jul 2026 00:00:00 -0400
Retrieval time2026-07-13T04:20:37.625Z
Last seen2026-07-13T04:20:37.625Z
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

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Computer Science > Computation and Language arXiv:2607.09094 (cs) [Submitted on 10 Jul 2026] Title:PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation Authors:Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar, Balaji Ganesan View a PDF of the paper titled PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation, by Devanshu Verma and 3 other authors View PDF HTML (experimental) Abstract: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.

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