An improved approach for generating summaries of judicial instruments

Abstract Summary generation technology can assist administrative law enforcement officers in grasping the core summary of administrative cases more quickly, which is one of the research focuses in the applied research of administrative justice. How to cope with the phenomenon of long-distance dependence on judicial documents and realize high-precision summary generation is an urgent problem to be solved at present. We propose a segmented judicial instrument summary generation model to solve the long-distance dependency problem. The model adopts a two-segment structure combining an extractive summary model and a generative summary model. Among them, the extractive summarization model consists of two parts: the BERT model and the document-level sentence encoder of the Transformer structure, which is used to initially process the judicial documents, extract the key legal information in the original documents, and compose the preliminary summaries, which are used as the training corpus for the generative summarization model. In addition, we introduce the contrast learning idea to alleviate the exposure bias problem in the generative model. The generative summary model takes T5-PEGASUS as the main body, generates pseudo-optimal summaries and multiple candidate summaries, and constructs contrast loss based on contrast learning to mitigate the exposure bias problem in the main model. Finally, the effectiveness of the algorithm is verified through experiments on the CAIL 2020 dataset (Challenge of AI in Law 2020) and the self-labelled AED summary dataset dedicated to administrative enforcement documents (Administrative Enforcement Documents Dataset).