将法律判例拆成短句片段，提升长文本检索精度
Legal Nugget Extraction for Granular Retrieval over Long Jurisprudential Texts
- 从判决书提取独立法律观点片段，按片段索引检索
- 在两个判例数据集上，NDCG@10提升超一倍，最高达0.327
- 适合以法律论点为查询的场景，不适用于所有法律检索
法院判决文书长度大、结构异质，关键法律主张可能仅占极小篇幅。本文探讨将法律主张（legal nuggets）——即从原文中提取的简短、自洽的法律论点——用于巴西法律语料的密集检索是否有效。提出一套流水线：先提取文档中的法律片段，用嵌入向量索引，检索片段级证据，再聚合结果回文档级排序。在JUA生态系统的四个葡萄牙语法律检索基准上评估，报告NDCG@10、MAP@10和MRR@10。在两个判例数据集上表现显著提升：在JUA-Juris上NDCG@10从0.10265增至0.20461，在JurisTCU上从0.20898升至0.32696。但在NormasTCU和BR-TaxQA上表现不及全文检索，且嵌入模型消融实验表明，强领域适配的检索器在全文设置下仍更优。结果表明，法律片段对判例搜索有帮助，尤其当查询为法律论点时，但未必普适于其他法律检索场景。
原文摘要 · Abstract (English)
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.
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