A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on mi-croblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from content-level structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the result of leader detection. To reduce the error propagation cascaded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the re-posting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.
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