A Methodology for Evaluating Timeline Generation Algorithms based on Deep Semantic Units

Abstract

Timeline generation is a summarisation task which transforms a narrative, roughly chronological input text into a set of timestamped summary sentences, each expressing an atomic historical event. We present a methodology for evaluating systems which create such timelines, based on a novel corpus consisting of 36 humancreated timelines. Our evaluation relies on deep semantic units which we call historical content units. An advantage of our approach is that it does not require human annotation of new system summaries.

Unsupervised Timeline Generation for Wikipedia History Articles

“…For evaluating our algorithms, the methodology we introduced in (Bauer and Teufel, 2015) is used, along with the accompanying Cambridge Single-Document Timeline Corpus (CSDTC, version 2.0), which has been made publicly available 1 .…”

The Storyline Annotation and Representation Scheme (StaR): A Proposal

“…Relevant information can be missed in this vast amount of data, leading to inconsistencies, fragmented reports, or gaps in the extraction and representation of complex stories. Different solutions have been proposed to deal with this problem ranging from the generation of multi-document extractive summaries (Barzilay et al, 1999), to clustering of news with respect to a topic (Swan and Allan, 2000), to the generation of timelines to monitor relevant events in a topic (Shahaf and Guestrin, 2010;Nguyen et al, 2014;Bauer and Teufel, 2015).…”