Classification of Sentence Ranking Methods for Multi-Document Summarization

Modern information technology allows text information to be produced and disseminated at a very rapid pace. This situation leads to the problem of information overload, in which users are faced with a very large body of text that is relevant to an information need and no efficient and effective way to locate within the body of text the specific information that is needed. In one example of such a scenario, a user might be given a collection of digital news articles relevant to a particular current event and may need to rapidly generate a summary of the essential information relevant to the event contained in those articles. In extractive MDS, the most fundamental task is to select a subset of the sentences in the input document set in order to form a summary of the document set. An essential component of this task is sentence ranking, in which sentences from the original document set are ranked in order of importance for inclusion in a summary. The purpose of this chapter is to give an analysis of the most successful methods for sentence ranking that have been employed in recent MDS work. To this end, the authors classify sentence ranking methods into six classes and present/discuss specific approaches within each class. Sean Sovine Marshall University, USA Hyoil Han Marshall University, USA

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