Timeline Summarization for Event-Related Discussions on a Chinese Social Media Platform.

In this paper, we proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event without supervised learning. In order to select event-related sentences, we designed a two-stage method to extract representative entity terms in the event-related discussions and filter out most of the sentences semantically un-related to the query event. A rule-based method was applied to extract sentences which describing sub-events. After that, the discussions are assigned to the corresponding sub-events according to the semantic relatedness measure. Finally, according to the occurring time of each sub-event, the timeline summarization is organized. We evaluated the performance of the proposed method on the real-world datasets. The experiment results showed that each processing step perform effectively. Especially, most noise sentences could be filtered by the proposed method. Moreover, the final timeline summarization graded by users is proven to be useful to well understand the discussion trend of a sub-event.