Extracting and Aligning Timelines

Abstract

. Understanding the timeline of a story is a necessary first step for extracting storylines. This is difficult, because timelines are not explicitly given in documents, and parts of a story may be found across multiple documents, either repeated or in fragments. We outline prior work and the state of the art in both timeline extraction and alignment of timelines across documents. With regard to timeline extraction, there has been significant work over the past 40 years on representing temporal information in text, but most of it has focused on temporal graphs and not timelines. In the past 15 years researchers have begun to consider the problem of extracting timelines from these graphs, but the approaches have been incomplete and inexact. We review these approaches and describe recent work of our own that solves timeline extraction exactly. With regard to timeline alignment, most efforts have been focused only on the specific task of cross-document event coreference (CDEC). Current approaches to CDEC fall into two camps: event–only clustering and joint event–entity clustering, with joint clustering using neural methods achieving state-of-the-art performance. All CDEC approaches rely on document clustering to generate a tractable search space. We note both shortcomings and advantages of these various approaches and, importantly, we describe how CDEC falls short of full timeline alignment extraction. We outline next steps to advance the field toward full timeline alignment across documents that can serve as a foundation for extraction of higher-level, more abstract storylines.