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Timeline Generation aims at summarizing news from different epochs andtelling readers how an event evolves. It is a new challenge that combinessalience ranking with novelty detection. For long-term public events, the maintopic usually includes various aspects across different epochs and each aspecthas its own evolving pattern. Existing approaches neglect such hierarchicaltopic structure involved in the news corpus in timeline generation. In thispaper, we develop a novel time-dependent Hierarchical Dirichlet Model (HDM) fortimeline generation. Our model can aptly detect different levels of topicinformation across corpus and such structure is further used for sentenceselection. Based on the topic mined fro HDM, sentences are selected byconsidering different aspects such as relevance, coherence and coverage. Wedevelop experimental systems to evaluate 8 long-term events that publicconcern. Performance comparison between different systems demonstrates theeffectiveness of our model in terms of ROUGE metrics.

Time-dependent Hierarchical Dirichlet Model for Timeline Generation