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Long-running, high-impact events such as the Boston Marathon bombing oftendevelop through many stages and involve a large number of entities in theirunfolding. Timeline summarization of an event by key sentences eases storydigestion, but does not distinguish between what a user remembers and what shemight want to re-check. In this work, we present a novel approach for timelinesummarization of high-impact events, which uses entities instead of sentencesfor summarizing the event at each individual point in time. Such entitysummaries can serve as both (1) important memory cues in a retrospective eventconsideration and (2) pointers for personalized event exploration. In order toautomatically create such summaries, it is crucial to identify the "right"entities for inclusion. We propose to learn a ranking function for entities,with a dynamically adapted trade-off between the in-document salience ofentities and the informativeness of entities across documents, i.e., the levelof new information associated with an entity for a time point underconsideration. Furthermore, for capturing collective attention for an entity weuse an innovative soft labeling approach based on Wikipedia. Our experiments ona real large news datasets confirm the effectiveness of the proposed methods.

Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events