Optimized Event Storyline Generation based on Mixture-Event-Aspect Model

Recently, much research focuses on event s-toryline generation, which aims to produce a concise, global and temporal event summary from a collection of articles. Generally, each event contains multiple sub-events and the sto-ryline should be composed by the componen-t summaries of all the sub-events. However, different sub-events have different part-whole relationship with the major event, which is important to correspond to users ’ interests but seldom considered in previous work. To distinguish different types of sub-events, we propose a mixture-event-aspect model which models different sub-events into local and global aspects. Combining these local/global aspects with summarization requirements to-gether, we utilize an optimization method to generate the component summaries along the timeline. We develop experimental systems on 6 distinctively different datasets. Evaluation and comparison results indicate the effective-ness of our proposed method.