Binding precedents (S\'umulas Vinculantes) constitute a juridical instrument
unique to the Brazilian legal system and whose objectives include the
protection of the Federal Supreme Court against repetitive demands. Studies of
the effectiveness of these instruments in decreasing the Court's exposure to
similar cases, however, indicate that they tend to fail in such a direction,
with some of the binding precedents seemingly creating new demands. We
empirically assess the legal impact of five binding precedents, 11, 14, 17, 26
and 37, at the highest court level through their effects on the legal subjects
they address. This analysis is only possible through the comparison of the
Court's ruling about the precedents' themes before they are created, which
means that these decisions should be detected through techniques of Similar
Case Retrieval. The contributions of this article are therefore twofold: on the
mathematical side, we compare the uses of different methods of Natural Language
Processing -- TF-IDF, LSTM, BERT, and regex -- for Similar Case Retrieval,
whereas on the legal side, we contrast the inefficiency of these binding
precedents with a set of hypotheses that may justify their repeated usage. We
observe that the deep learning models performed significantly worse in the
specific Similar Case Retrieval task and that the reasons for binding
precedents to fail in responding to repetitive demand are heterogeneous and
case-dependent, making it impossible to single out a specific cause.

巴西法律体系中的约束性先例（S'umulas Vinculantes）是一种独特的司法工具，其目标包括保护联邦最高法院免受重复要求的侵害。然而，关于这些工具在减少法院面临类似案件方面的有效性的研究表明，它们往往无法达到此目标，其中一些约束性先例似乎创造了新的需求。通过对 5 个约束性先例（11、14、17、26 和 37）在最高法院层面上对其涉及的法律主题产生的影响进行实证评估，我们可以比较不同自然语言处理方法（TF-IDF、LSTM、BERT 和正则表达式）在类似案例检索方面的应用，并对这些约束性先例的低效性与一组可能解释其重复使用的假设进行对比。我们观察到深度学习模型在具体的类似案例检索任务中表现明显较差，并且约束性先例在应对重复需求方面的原因是多样的，与具体案件相关，这使得我们无法确定具体的原因。

通过相似案例检索对巴西最高法院中的招标先例效率的实证分析

Empirical analysis of Biding Precedent efficiency in the Brazilian  Supreme Court via Similar Case Retrieval

Similar case retrieval (SCR) is a representative legal AI application that
plays a pivotal role in promoting judicial fairness. However, existing SCR
datasets only focus on the fact description section when judging the similarity
between cases, ignoring other valuable sections (e.g., the court's opinion)
that can provide insightful reasoning process behind. Furthermore, the case
similarities are typically measured solely by the textual semantics of the fact
descriptions, which may fail to capture the full complexity of legal cases from
the perspective of legal knowledge. In this work, we present MUSER, a similar
case retrieval dataset based on multi-view similarity measurement and
comprehensive legal element with sentence-level legal element annotations.
Specifically, we select three perspectives (legal fact, dispute focus, and law
statutory) and build a comprehensive and structured label schema of legal
elements for each of them, to enable accurate and knowledgeable evaluation of
case similarities. The constructed dataset originates from Chinese civil cases
and contains 100 query cases and 4,024 candidate cases. We implement several
text classification algorithms for legal element prediction and various
retrieval methods for retrieving similar cases on MUSER. The experimental
results indicate that incorporating legal elements can benefit the performance
of SCR models, but further efforts are still required to address the remaining
challenges posed by MUSER. The source code and dataset are released at
this https URL

该研究论文介绍了 MUSER，它是一个基于多视角相似度度量和综合法律要素的类似案例检索数据集，该数据集源自中国民事案件，包含 100 个查询案例和 4,024 个候选案例。实验结果显示，引入法律要素可以改善类似案例检索模型的性能，但仍需要进一步努力解决 MUSER 所带来的挑战。