A Participant-based Approach for Event Summarization Using Twitter Streams

Chao Shen, Fei Liu, Fuliang Weng and Tao Li

Twitter offers an unprecedented advantage on live reporting of the events happening around the world. However, summarizing the Twitter event has been a challenging task that was not fully explored in the past. In this paper, we propose a participant-based event summarization approach that "zooms-in" the Twitter event streams to the participant level, detects the important sub-events associated with each participant using a novel mixture model that combines the "burstiness" and "cohesiveness" properties of the event tweets, and generates the event summaries progressively. We evaluate the proposed approach on different event types. Results show that the participant-based approach can effectively capture the sub-events that have otherwise been shadowed by the long-tail of other dominant sub-events, yielding summaries with considerably better coverage than the state-of-the-art.

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