A multi-label legal charge prediction method based on summarization generation

Abstract

Legal Charge Prediction (LCP) is a task of predicting charge labels for which a criminal should be charged based on the factual case descriptions. In recent years, the advancement of deep learning networks has led to the emergence of effective approaches for LCP through the Fine-tuning of pre-trained language models. While existing methods primarily treat it as multiple binary classifications, excelling in the single charge label given factual case descriptions, they still exhibit substantial room for improvement in multi-label LCP. This is because single-label classification methods do not effecitively deal with the relationship between multiple labels, mainly determining the likelihood of a factual description’s association to each individual charge label. Recognizing the need to enhance the relationship among multi-label charges in Fine-tuning-based discriminative charge prediction, this paper introduces a multi-label LCP method based on summarization generation. This method reconstructs the multi-label classification problem as a summarization generation task, capturing the relationship among multiple labels within the summarization content. Empirical findings from the Chinese legal domain’s CAIL2018 dataset demonstrate the method’s capacity to discern the relationship between multiple legal charge labels with Micro-F1 and Macro-F1 enhancements, compared to the fine-tuning-based approaches.