Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

作     者：Ren Tao Chen Shuang 

作者机构：Software CollegeNortheastern UniversityShenyang110000China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第6期

页      面：6201-6217页

核心收录：

学科分类：0303[法学-社会学] 1205[管理学-图书情报与档案管理] 03[法学] 030303[法学-人类学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China(62276058,61902057,41774063) Fundamental Research Funds for the Central Universities(N2217003) Joint Fund of Science&Technology Department of Liaoning Province and State Key Laboratory of Robotics,China(2020-KF-12-11). 

主　　题：Automatic summarization abstractive summarization weakly supervised training entity recognition 

摘      要：A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.