Timeline Summarization from Social Media with Life Cycle Models

Abstract

The popularity of social media shatters the barrier for online users to create and share information at any place at any time. As a consequence, it has become increasing difficult to locate relevance information about an entity. Timeline has been proven to provide an effective and efficient access to understand an entity by displaying a list of episodes about the entity in chronological order. However, summarizing the timeline about an entity with social media data faces new challenges. First, key timeline episodes about the entity are typically unavailable in existing social media services. Second, the short, noisy and informal nature of social media posts determines that only content-based summarization could be insufficient. In this paper, we investigate the problem of timeline summarization and propose a novel framework Timeline-Sumy, which consists of episode detecting and summary ranking. In episode detecting, we explicitly model temporal information with life cycle models to detect timeline episodes since episodes usually exhibit sudden-rise-and-heavy-tail patterns on time-series. In summary ranking, we rank social media posts in each episode via a learning-to-rank approach. The experimental results on social media datasets demonstrate the effectiveness of the proposed framework.