论文标题
从判断的前提到要点
From Judgement's Premises Towards Key Points
论文作者
论文摘要
关键点分析（KPA）是NLP中一个相对较新的任务，它通过从文本集合中提取论证关键点（KP）来结合摘要和分类，并将其与不同参数的亲密关系分类。在我们的工作中，我们专注于法律领域，并开发方法，这些方法从判断文本得出的前提中识别和提取KP。第一种方法是适应现有的最新方法，另外两种方法是我们从头开始开发的新方法。我们介绍了它们的产出的方法和示例，以及它们之间的比较。对我们结果的完整评估是在匹配任务中完成的 - 生成的KPS与参数（前提）之间进行了匹配。
Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).
