A Knowledge-enhanced Generative Summary Model for Audit News - ZHU Siwen; ZHANG Yangsen; WANG Xuesong; SUN Longyuan; XU Ruiyi; JIA Qilong (Journal of Harbin University of Science and Technology)
To address the problem that existing summary generation models do not fully understand audit news and tend to lose key information, a summary generation model (text rank and bart with knowledge enhancement model, TRB-KE) that combines knowledge enhancement and generative summary model is proposed. Then, a set of audit domain knowledge base is built and the terms contained in the news are extracted with their meanings and incorporated into the generative summary model as background knowledge. Finally, the generative summary model is used to summarize the high-quality news texts with background knowledge and obtain the summary results. At the same time, a set of audit news dataset is constructed for targeted training to improve the model effect. The experimental results show that compared with the benchmark model, the proposed TRB-KE model improves the mean Rouge value by 0. 98% and 1. 02% in the audit news dataset and the NLPCC2018 dataset, respectively, which proves that the proposed model can learn the deep information of the news and improve the quality of the generated summary.