Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
Autor: | Ujwal Gadiraju, Tuan Tran, Nattiya Kanhabua, Claudia Niederée, Avishek Anand |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
FOS: Computer and information sciences
Information retrieval Computer Science - Computation and Language Event (computing) Computer science Rank (computer programming) Novelty Timeline 02 engineering and technology Automatic summarization Ranking (information retrieval) Computer Science - Information Retrieval Ranking H.3.3 Salience (neuroscience) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Adaptive learning Computation and Language (cs.CL) Information Retrieval (cs.IR) |
Popis: | 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. Published via ACM to CIKM 2015 |
Databáze: | OpenAIRE |
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