Extractive summarisation via sentence removal: condensing relevant sentences into a short summary

Authors:
Marco Bonzanini;Miguel Martinez-Alvarez;Thomas Roelleke
Affiliations:
Queen Mary University of London, London, United Kingdom;Queen Mary University of London, London, United Kingdom;Queen Mary University of London, London, United Kingdom
Venue:
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Year:
2013

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Abstract

Many on-line services allow users to describe their opinions about a product or a service through a review. In order to help other users to find out the major opinion about a given topic, without the effort to read several reviews, multi-document summarisation is required. This research proposes an approach for extractive summarisation, supporting different scoring techniques, such as cosine similarity or divergence, as a method for finding representative sentences. The main contribution of this paper is the definition of an algorithm for sentence removal, developed to maximise the score between the summary and the original document. Instead of ranking the sentences and selecting the most important ones, the algorithm iteratively removes unimportant sentences until a desired compression rate is reached. Experimental results show that variations of the sentence removal algorithm provide good performance.