Update Summarization using a Multi-level Hierarchical Dirichlet Process Model

Update summarization is a new challenge which combines salience ranking with novelty detection. Previous researches usually convert novelty detection to the problem of redundancy removal or salience re-ranking, and seldom explore the birth, splitting, merging and death of aspects for a given topic. In this paper, we borrow the idea of evolutionary clustering and propose a three-level HDP model named h-uHDP, which reveals the diversity and commonality between aspects discovered from two different epochs (i.e. epoch history and epoch update). Specifically, we strengthen modeling the sentence level in the h-uHDP model to adapt to the sentence extraction based framework. Automatic and manual evaluations on TAC data demonstrate the effectiveness of our update summarization algorithm, especially from the novelty criterion.