A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations

TL;DR

This paper introduces a novel unsupervised dynamic framework utilizing distributed representations for timeline generation from timestamped web documents, effectively capturing evolving topics and outperforming existing methods.

Contribution

A new evolutionary timeline generation framework based on distributed representations that dynamically finds the most likely sequence of summaries, improving coherence and relevance.

Findings

Outperforms various competitive baselines

Achieves state-of-the-art performance in timeline generation

Demonstrates feasibility of unsupervised summarization approach

Abstract

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.