A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations
This addresses timeline generation for web documents, offering an unsupervised method that improves over existing approaches, though it appears incremental in its advancements.
The authors tackled timeline summarization by introducing a dynamic framework that uses distributed representations to find the most likely sequence of evolutionary summaries, reducing irrelevant or repeated events. Experimental results show it outperforms competitive baselines and achieves state-of-the-art performance as an unsupervised approach.
Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous work focuses on fully-observable ranking models and depends on hand-designed features or complex mechanisms that may not generalize well. We present a novel dynamic framework for evolutionary timeline generation leveraging distributed representations, which dynamically finds the most likely sequence of evolutionary summaries in the timeline, called the Viterbi timeline, and reduces the impact of events that irrelevant or repeated to the topic. The assumptions of the coherence and the global view run through our model. We explore adjacent relevance to constrain timeline coherence and make sure the events evolve on the same topic with a global view. Experimental results demonstrate that our framework is feasible to extract summaries for timeline generation, outperforms various competitive baselines, and achieves the state-of-the-art performance as an unsupervised approach.