Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty

McCreadie, R., Macdonald, C. and Ounis, I. (2014) Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty. In: CIKM '14: 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China, 3-7 Nov 2014, pp. 301-310. ISBN 9781450325981 (doi:10.1145/2661829.2661951)

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Publisher's URL: http://dx.doi.org/10.1145/2661829.2661951

Abstract

The automatic summarization of long-running events from news steams is a challenging problem. A long-running event can contain hundreds of unique 'nuggets' of information to summarize, spread-out over its lifetime. Meanwhile, information reported about it can rapidly become outdated and is often highly redundant. Incremental update summarization (IUS) aims to select sentences from news streams to issue as updates to the user, summarising that event over time. The updates issued should cover all of the key nuggets concisely and before the information contained in those nuggets becomes outdated. Prior summarization approaches when applied to IUS can fail, since they define a fixed summary length that cannot effectively account for the different magnitudes and varying rate of development of such events. In this paper, we propose a novel IUS approach that adaptively alters the volume of content issued as updates over time with respect to the prevalence and novelty of discussions about the event. It incorporates existing state-of-the-art summarization techniques to rank candidate sentences, followed by a supervised regression model that balances novelty, nugget coverage and timeliness when selecting sentences from the top ranks. We empirically evaluate our approach using the TREC 2013 Temporal Summarization dataset extended with additional assessments. Our results show that by adaptively adjusting the number of sentences to select over time, our approach can nearly double the performance of effective summarization baselines.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Mccreadie, Mr Richard
Authors: McCreadie, R., Macdonald, C., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450325981
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
624701SUPERIadh OunisEuropean Commission (EC)606853COM - COMPUTING SCIENCE
570861SMART: Search engine for MultimediA enviRonment generated contenTIadh OunisEuropean Commission (EC)N/ACOM - COMPUTING SCIENCE