Evolutionary Hierarchical Dirichlet Process for Timeline Summarization

Timeline summarization aims at generating concise summaries and giving readers a faster and better access to understand the evolution of news. It is a new challenge which combines salience ranking problem with novelty detection. Previous researches in this field seldom explore the evolutionary pattern of topics such as birth, splitting, merging, developing and death. In this paper, we develop a novel model called Evolutionary Hierarchical Dirichlet Process(EHDP) to capture the topic evolution pattern in timeline summarization. In EHDP, time varying information is formulated as a series of HDPs by considering time-dependent information. Experiments on 6 different datasets which contain 3156 documents demonstrates the good performance of our system with regard to ROUGE scores.