Automatic Multi-Document Summarization Based on Keyword Density and Sentence-Word Graphs

作     者：YE Feiyue XU Xinchen 叶飞跃;徐欣辰

作者机构：School of Computer Engineering and ScienceShanghai UniversityShanghai 200444China 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2018年第23卷第4期

页      面：584-592页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：multi-document graph algorithm keyword density Graph & Keywordρ Duc2004 

摘      要：As a fundamental and effective tool for document understanding and organization, multi-document summarization enables better information services by creating concise and informative reports for large collections of documents. In this paper, we propose a sentence-word two layer graph algorithm combining with keyword density to generate the multi-document summarization, known as Graph ＆ Keywordp. The traditional graph methods of multi-document summarization only consider the influence of sentence and word in all documents rather than individual documents. Therefore, we construct multiple word graph and extract right keywords in each document to modify the sentence graph and to improve the significance and richness of the summary. Meanwhile, because of the differences in the words importance in documents, we propose to use keyword density for the summaries to provide rich content while using a small number of words. The experiment results show that the Graph ＆ Keywordp method outperforms the state of the art systems when tested on the Duc2004 data set. Key words： multi-document, graph algorithm, keyword density, Graph ＆ Keywordp, Due2004