# Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries

arXiv: https://arxiv.org/abs/2508.00679 · HTML: https://mlsanity.com/paper/2508.00679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering](https://mlsanity.com/paper/2404.04302/) (2024)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding](https://mlsanity.com/paper/2305.05393/) (2023)
- [CaseLink: Inductive Graph Learning for Legal Case Retrieval](https://mlsanity.com/paper/2403.17780/) (2024)
- [nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models](https://mlsanity.com/paper/2204.07853/) (2022)
- [CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs](https://mlsanity.com/paper/2312.11229/) (2023)
- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India](https://mlsanity.com/paper/2502.16573/) (2025)
- [PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval](https://mlsanity.com/paper/2201.01614/) (2022)
- [LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases](https://mlsanity.com/paper/2501.14114/) (2025)

## Built on by (most cited first, 1 of 1)

- [Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act](https://mlsanity.com/paper/2606.20359/) (2026)

## Abstract (arXiv)

Legal precedent retrieval is a cornerstone of the common law system, governed by the principle of stare decisis, which demands consistency in judicial decisions. However, the growing complexity and volume of legal documents challenge traditional retrieval methods. TraceRetriever mirrors real-world legal search by operating with limited case information, extracting only rhetorically significant segments instead of requiring complete documents. Our pipeline integrates BM25, Vector Database, and Cross-Encoder models, combining initial results through Reciprocal Rank Fusion before final re-ranking. Rhetorical annotations are generated using a Hierarchical BiLSTM CRF classifier trained on Indian judgments. Evaluated on IL-PCR and COLIEE 2025 datasets, TraceRetriever addresses growing document volume challenges while aligning with practical search constraints, reliable and scalable foundation for precedent retrieval enhancing legal research when only partial case knowledge is available.