Abstract
Storyline detection from news articles aims at summarizing events described under a certain news topic and revealing how those events evolve over time. It is a difficult task because it requires first the detection of events from news articles published in different time periods and then the construction of storylines by linking events into coherent news stories. Moreover, each storyline has different hierarchical structures which are dependent across epochs. Existing approaches often ignore the dependency of hierarchical structures in storyline generation. In this paper, we propose an unsupervised Bayesian model, called dynamic storyline detection model, to extract structured representations and evolution patterns of storylines. The proposed model is evaluated on a large scale news corpus. Experimental results show that our proposed model outperforms several baseline approaches.
Highlights
The rapid development of online news media sites is accompanied by the generation of tremendous news reports
Facing such massive amount of news articles, it is crucial to develop an automated tool which can provide a temporal summary of events and their evolutions related to a topic from news reports
Storyline detection, aiming at summarising the development of certain related events, has been studied in order to help readers quickly understand the major events reported in news articles
Summary
The rapid development of online news media sites is accompanied by the generation of tremendous news reports. Facing such massive amount of news articles, it is crucial to develop an automated tool which can provide a temporal summary of events and their evolutions related to a topic from news reports. Storyline detection, aiming at summarising the development of certain related events, has been studied in order to help readers quickly understand the major events reported in news articles. Radinsky and Horvitz (2013) built storylines based on text clustering and entity entropy to predict future events. Huang and Huang (2013) developed a mixture-event-aspect model to model sub-events into local and global aspects and utilize an optimization method to generate storylines. Wang et al (2013) proposed an evolutionary multi-branch tree clustering method for streaming text data in which the tree construction is casted as an online posterior estimation problem by considering both the current tree and the previous tree simultaneously
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