Tracking Events Using Time-dependent Hierarchical Dirichlet Tree Model

Timeline Generation, through generating news timelines from the massive data of news corpus, aims at providing readers with summaries about the evolvement of an event. It is a new challenge of summarization that combines salience ranking with novelty detection. For a long-term public event, the main topic usually includes many different sub-topics at varying epochs, which also has its own evolving patterns. Existing approaches fail to utilize such hierarchical topic structure involved in the news corpus for timeline generation . In this paper, we develop a novel time-dependent Hierarchical Dirichlet Tree Model (tHDT) for timeline generation. Our model can aptly detect different levels of topic information in corpus and the structure is further used for sentence selection. Based on the topic distribution mined from tHDT, sentences are selected through an overall consideration of relevance, coherence and coverage. We develop experimental systems to compare different rival algorithms on 8 long-term events of public concern. The performance comparison demonstrates the effectiveness of our proposed model in terms of ROUGE metrics.