Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024)

Abstract: The Criminal Court View Generation task aims to produce explanations that inform judicial decisions. This necessitates a nuanced understanding of diverse legal concepts, such as Recidivism, Confess, and Robbery, which often coexist within cases, complicating holistic analysis. However, existing methods mainly rely on the generation capability of language models, without paying enough attention to the important legal concepts.To enhance the precision and depth of such explanations, we introduce Legal Concept-guided Criminal Court Views Generation (LeGen), a three-stage approach designed for iterative reasoning tailored to individual legal constructs.Specifically, in the first stage, we design a decomposer to divide the court views into focused sub-views, each anchored around a distinct legal concept. Next, a concept reasoning module generates targeted rationales by intertwining the deconstructed facts with their corresponding legal frameworks, ensuring contextually relevant interpretations.Finally, a verifier and a generator are employed to align the rationale with the case fact and obtain synthesized comprehensive and legally sound final court views, respectively.We evaluate LeGen by conducting extensive experiments on a real-world dataset and experimental results validate the effectiveness of our proposed model. Our codes are available at https://anonymous.4open.science/r/LeGen-5625 .

## introduction the criminal court view generation task aims to automatically generate rationales supporting final legal judgments @xcite @xcite @xcite . essentially, this involves automatically generating accurate, clear, and logical texts that summarize criminal case facts and justify legal verdicts, such as charges and sentencing. it ## conclusion in this study, we introduce a legal concept-guided court view generation framework (legen). specifically, given the fact, the legen first divides the court view into several sub-views based on the predicted legal concepts. then the solver and verifier are employed to generate and select rationales respectively. finally, the court view is generated by incorporating the fact and rationales. comprehensive experiments demonstrate the effectiveness of our proposed method.