Exploring the Interactions of Storylines from Informative News Events  

作　　者：胡珀 黄民烈 朱小燕 

机构地区：[1]State Key Laboratory of Intelligent Technology and Systems,Department of Computer Science and Technology Tsinghua University

出　　处：《Journal of Computer Science & Technology》2014年第3期502-518,共17页计算机科学技术学报（英文版）

基　　金：Supported by the National Basic Research 973 Program of China under Grant No.2012CB316301;the National Natural Science Foundation of China under Grant No.60803075;the Tsinghua University Initiative Scientific Research Program under Grant No.20121088071;the Beijing Higher Education Young Elite Teacher Project

摘　　要：Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.

关 键 词：text mining storyline interaction informative event COHERENCE user preference 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]