Understanding the timeline of a story is a necessary first step for extracting storylines. This is difficult because timelines are rarely explicitly given in documents, and fragments of a story may be found across multiple documents. We outline prior work and the state of the art in both timeline extraction and alignment of events across documents. Previous work focused mainly on temporal graph extraction rather than actual timelines. Recently, there has been a growing interest in extracting timelines from these graphs. We review this work and describe our own approach that solves timeline extraction exactly. With regard to event alignment, most efforts have focused on the specific task of cross-document event coreference (CDEC). Current approaches to CDEC perform either event-only clustering or joint event–entity clustering, with neural methods achieving the best results. 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.

A variety of approaches exist for annotating temporal and event information in text, but it has been difficult to compare and contrast these different corpora. The Richer Event Description (RED) corpus, as an ambitious annotation of temporal, causal, and coreference annotation, provides one point of comparison for discussing how different annotation decisions contribute to the timeline and causal chains which define a document. We present an overview of how different event corpora differ and present new methods for studying the impact of these temporal annotation decisions upon the resulting document timeline. This focus on illuminating the contribution of three particular sources of information – event coreference, causal relations with temporal information, and long-distance temporal containment – to the actual timeline of a document. By studying the impact of specific annotation strategies and framing the RED annotation in the larger context of other event–event relation annotation corpora, we hope to provide a clearer sense of the current state of event annotation and of promising future directions for annotation.