  Long-running, high-impact events such as the Boston Marathon bombing often
develop through many stages and involve a large number of entities in their
unfolding. Timeline summarization of an event by key sentences eases story
digestion, but does not distinguish between what a user remembers and what she
might want to re-check. In this work, we present a novel approach for timeline
summarization of high-impact events, which uses entities instead of sentences
for summarizing the event at each individual point in time. Such entity
summaries can serve as both (1) important memory cues in a retrospective event
consideration and (2) pointers for personalized event exploration. In order to
automatically 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 of
entities and the informativeness of entities across documents, i.e., the level
of new information associated with an entity for a time point under
consideration. Furthermore, for capturing collective attention for an entity we
use an innovative soft labeling approach based on Wikipedia. Our experiments on
a real large news datasets confirm the effectiveness of the proposed methods.
