用无监督方法自动为巴西特备上诉案匹配法律主题，提升司法效率。
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
- 基于改进的图算法生成上诉案摘要，无需标注数据
- 通过BM25计算摘要与主题相似度，实现精准主题排序
- 适合法律科技从业者及司法自动化研究者
巴西宪法，即《公民宪章》，赋予公民向司法机构提出诉求的机制，其中包括所谓的特备上诉。此类上诉旨在统一巴西立法的法律解释，适用于判决与联邦法律相悖的情形。处理特备上诉是司法系统的日常任务，常带来大量案件压力。本文提出一种名为GLARE的新方法，基于无监督机器学习，帮助法律分析师从巴西国家法院（STJ）提供的主题列表中为特备上诉分类。该方法引入了对图基LexRank算法的改进版本——引导式LexRank（Guided LexRank），用于生成特备上诉的摘要。通过BM25算法评估生成摘要与各主题之间的相似度，从而输出最相关的主题排名。该方法无需预先标注文本，也不依赖大规模训练数据。我们通过将该方法应用于先前由人工专家分类过的特备上诉语料库，验证了其有效性。
原文摘要 · Abstract (English)
The Brazilian Constitution, known as the Citizen's Charter, provides mechanisms for citizens to petition the Judiciary, including the so-called special appeal. This specific type of appeal aims to standardize the legal interpretation of Brazilian legislation in cases where the decision contradicts federal laws. The handling of special appeals is a daily task in the Judiciary, regularly presenting significant demands in its courts. We propose a new method called GLARE, based on unsupervised machine learning, to help the legal analyst classify a special appeal on a topic from a list made available by the National Court of Brazil (STJ). As part of this method, we propose a modification of the graph-based LexRank algorithm, which we call Guided LexRank. This algorithm generates the summary of a special appeal. The degree of similarity between the generated summary and different topics is evaluated using the BM25 algorithm. As a result, the method presents a ranking of themes most appropriate to the analyzed special appeal. The proposed method does not require prior labeling of the text to be evaluated and eliminates the need for large volumes of data to train a model. We evaluate the effectiveness of the method by applying it to a special appeal corpus previously classified by human experts.
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