Paper Info
Title
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
Abstract
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.
Year
DOI
Venue
2015
10.1145/2806416.2806486
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Field
DocType
Volume
Pointer (computer programming),Automatic summarization,Learning to rank,Data mining,Ranking,Information retrieval,Computer science,Timeline,Novelty,Salience (language),Adaptive learning
Conference
abs/1701.03947
Citations 
PageRank 
References 
9
0.52
24
Authors
5
Name
Order
Citations
PageRank
Tuan Tran1333.43
Claudia Niederée2343.50
Nattiya Kanhabua334626.35
Ujwal Gadiraju45910.46
Avishek Anand510211.61