Timeline Summarization for Event-relate Facts and Public Issues on a ChineseSocial Media Platform
Abstract: In this work, I proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event withou tsupervised learning. In order to select event-related sentences, I 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 thequery event. A rule-based method was applied to extract sentences 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. The performance of the proposed method is evaluated based on real-world datasets.The experiment results showed that each processing step performs effectively. Especially, most noise sentences could be filtered by the proposed method.Moreover, the final timeline summarization graded by users is proven to be usefulto well understand the discussion trend of a sub-event.
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