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

TL;DR

This paper introduces an adaptive learning-to-rank method for selecting key entities in timeline summarization of high-impact events, enhancing personalized event exploration and memory cues.

Contribution

It presents a novel entity ranking approach that balances salience and informativeness, using dynamic adaptation and Wikipedia-based attention modeling.

Findings

Effective entity ranking improves timeline summaries.

Enhanced personalization and memory cues in event timelines.

Validated on large news datasets with positive results.

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.