Unlocking authentic judicial reasoning: A Template-Based Legal Information Generation framework for judicial views

Abstract

In the field of legal AI, the research on the task of generating judicial views is relatively weak. Judicial views typically include judgment information on the merits such as charges, law articles, and sentencing circumstances. There are several issues with using universal algorithms to generate text, such as a lack of professional expression required for legal documents, and missing or incorrect judgment information on the merits. Judicial views often involve judgment information on the merits, rather than summarizing and excerpting known information. This could be why the application of conventional text generation algorithms may not produce satisfactory results. In actual judicial scenarios, judges generate their views step by step. Firstly, identify multiple judgment information on the merits in the judicial views, and then connect them with legal templates to achieve the generation of judicial views. This article proposes a Template Based Legal Information Generation (TBLIG) framework that generates higher quality judicial views based on the authentic judicial reasoning methods used by judges. The method consists of two steps: The first step involves determining the judgment information on the merits in the judicial view, while the second step involves synchronously generating a legal template based on the case. We have carried out a large number of experiments on the dataset of indictments and non-indictments. Through comparison with other models and ablation experiments, we have proved the effectiveness of our proposed method.