Time-dependent Hierarchical Dirichlet Model for Timeline Generation
This work addresses the problem of summarizing evolving news events for readers by incorporating hierarchical topic modeling, though it appears incremental as it builds on existing timeline generation methods.
The paper tackles timeline generation for long-term public events by proposing a time-dependent Hierarchical Dirichlet Model (HDM) to detect hierarchical topic structures across epochs, which improves sentence selection based on relevance, coherence, and coverage, resulting in enhanced performance as demonstrated by ROUGE metrics on 8 events.
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.