Legal Nugget Extraction for Granular Retrieval over Long Jurisprudential Texts

2026-07-24

AI Summary

Judicial case documents are typically lengthy, with critical legal arguments constituting only a small fraction of the text, which limits the effectiveness of conventional full-text retrieval. This work proposes a fine-grained retrieval framework that, for the first time, integrates legal nugget extraction with dense retrieval. The approach first automatically extracts self-contained legal nuggets from case documents, constructs an embedding index at the nugget level, and performs dense retrieval over these nuggets; results are then aggregated to produce document-level rankings. Evaluated on the JUA-Juris and JurisTCU datasets, the method achieves NDCG@10 scores of 0.20461 and 0.32696, respectively, significantly outperforming baseline approaches and enabling precise case retrieval tailored to legal argument queries.

Abstract

Legal retrieval over jurisprudential collections is challenging because court decisions are long, heterogeneous documents whose relevant legal thesis may occupy only a small portion of the text. This paper asks whether legal nuggets, defined as short and self-contained legal theses extracted from source documents, can improve dense retrieval over Brazilian legal collections. We propose a pipeline that extracts nuggets from each document, indexes them with embeddings, retrieves nugget-level evidence, and aggregates the retrieved nuggets back to document-level rankings. We evaluate this approach on four Portuguese legal retrieval benchmarks from the JUA ecosystem, reporting NDCG@10, MAP@10, and MRR@10. Nugget retrieval substantially improves the two jurisprudential datasets: on JUA-Juris, NDCG@10 increases from 0.10265 to 0.20461, and on JurisTCU from 0.20898 to 0.32696. However, it underperforms full-document retrieval on NormasTCU and BR-TaxQA, and an embedding-model ablation shows that strong domain-adapted retrievers can remain better in the full-document setting. The results demonstrate that legal nuggets can be useful for jurisprudence search, especially when queries are formulated as legal theses, but they may not transfer equally well to other legal retrieval scenarios.

Problem

• legal retrieval
• jurisprudential texts
• legal nuggets
• dense retrieval
• granular retrieval

Innovation

• legal nugget extraction
• dense retrieval
• jurisprudential text
• fine-grained retrieval
• embedding-based ranking