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Durham University

Computer Science


Publication details for Professor Alexandra Cristea

Zhou, Yiwei Kanhabua, N. & Cristea, A. I. (2016), Real-time timeline summarisation for high-impact events in Twitter, in Kaminka, Gal A. Fox, Maria Bouquet, Paolo Hüllermeier, Eyke Dignum, Virginia Dignum, Frank & Harmelen, Frank van eds, Frontiers in Artificial Intelligence and Applications 285: ECAI 2016. The Hague, IOS Press, 1158-1166.

Author(s) from Durham


Twitter has become a valuable source of event-related
information, namely, breaking news and local event reports. Due
to its capability of transmitting information in real-time, Twitter is
further exploited for timeline summarisation of high-impact events,
such as protests, accidents, natural disasters or disease outbreaks.
Such summaries can serve as important event digests where users
urgently need information, especially if they are directly affected by
the events. In this paper, we study the problem of timeline summarisation
of high-impact events that need to be generated in real-time.
Our proposed approach includes four stages: classification of realworld
events reporting tweets, online incremental clustering, postprocessing
and sub-events summarisation. We conduct a comprehensive
evaluation of different stages on the “Ebola outbreak” tweet
stream, and compare our approach with several baselines, to demonstrate
its effectiveness. Our approach can be applied as a replacement
of a manually generated timeline and provides early alarms for disaster