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Given the collection of timestamped web documents related to the evolvingtopic, timeline summarization (TS) highlights its most important events in theform of relevant summaries to represent the development of a topic over time.Most of the previous work focuses on fully-observable ranking models anddepends on hand-designed features or complex mechanisms that may not generalizewell. We present a novel dynamic framework for evolutionary timeline generationleveraging distributed representations, which dynamically finds the most likelysequence of evolutionary summaries in the timeline, called the Viterbitimeline, and reduces the impact of events that irrelevant or repeated to thetopic. The assumptions of the coherence and the global view run through ourmodel. We explore adjacent relevance to constrain timeline coherence and makesure the events evolve on the same topic with a global view. Experimentalresults demonstrate that our framework is feasible to extract summaries fortimeline generation, outperforms various competitive baselines, and achievesthe state-of-the-art performance as an unsupervised approach.

A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations