Automatic content curation of news events

With the rapid development of the internet, a large amount of online news has brought readers a variety of information. Some important events last for some time as the event develops or the topic spreads. When readers want to catch up on the details of a specific news event, most of them use a search engine to collect news and understand the whole story. It usually takes readers a considerable amount of time to sort out the causes and effects of the event. The general method of online news provision aggregates and organizes the content of news articles from a large number of events and presents the content to readers. Most of this type of information is manually organized. To solve these problems, this study proposes an automated method of news curation. First, we extract the topics from the event data set and use word sequences to find the sequence of topic transfer through a hidden Markov model. Second, we calculate the strength of the topic and the variation in the strength to detect important time points during the development of the news event. Finally, a concise summary is generated at each time point. This paper combines two characteristics, chronology and summary, to design a curation method that can effectively help readers quickly grasp the context of a news event. The experimental results show that the method has good performance in each module, such as the detection of the important phases of events and the creation of the news summary.