Summarization of historical articles using temporal event clustering

Authors:
James Gung;Jugal Kalita
Affiliations:
Miami University, Oxford, Ohio;University of Colorado, Colorado Springs CO
Venue:
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Year:
2012

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Abstract

In this paper, we investigate the use of temporal information for improving extractive summarization of historical articles. Our method clusters sentences based on their timestamps and temporal similarity. Each resulting cluster is assigned an importance score which can then be used as a weight in traditional sentence ranking techniques. Temporal importance weighting offers consistent improvements over baseline systems.