  Timeline Generation aims at summarizing news from different epochs and
telling readers how an event evolves. It is a new challenge that combines
salience ranking with novelty detection. For long-term public events, the main
topic usually includes various aspects across different epochs and each aspect
has its own evolving pattern. Existing approaches neglect such hierarchical
topic structure involved in the news corpus in timeline generation. In this
paper, we develop a novel time-dependent Hierarchical Dirichlet Model (HDM) for
timeline generation. Our model can aptly detect different levels of topic
information across corpus and such structure is further used for sentence
selection. Based on the topic mined fro HDM, sentences are selected by
considering different aspects such as relevance, coherence and coverage. We
develop experimental systems to evaluate 8 long-term events that public
concern. Performance comparison between different systems demonstrates the
effectiveness of our model in terms of ROUGE metrics.
