Unsupervised Timeline Generation for Wikipedia History Articles

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Bauer, S 
Teufel, SH 

This paper presents a generic approach to content selection for creating timelines from individual history articles for which no external information about the same topic is available. This scenario is in contrast to existing works on timeline generation, which require the presence of a large corpus of news articles. To identify salient events in a given history article, we exploit lexical cues about the article's subject area, as well as time expressions that are syntactically attached to an event word. We also test different methods of ensuring timeline coverage of the entire historical time span described. Our best-performing method outperforms a new unsupervised base-line and an improved version of an existing supervised approach. We see our work as a step towards more semantically motivated approaches to single-document summarisation.

Journal Title
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Conference Name
EMNLP 2016: Conference on Empirical Methods in Natural Language Processing
Journal ISSN
Volume Title
Association for Computational Linguistics