Aksoy, CemBuğdaycı, AhmetGür, TunayUysal, İbrahimCan, Fazlı2016-02-082016-02-082009-09http://hdl.handle.net/11693/28644Date of Conference: 14-16 Sept. 2009Conference name: 24th International Symposium on Computer and Information Sciences, 2009Semantic Role Labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates. In this paper, we introduce and assess the idea of using SRL on generic Multi-Document Summarization (MDS). We score sentences according to their inclusion of frequent semantic phrases and form the summary using the top-scored sentences. We compare this method with a term-based sentence scoring approach to investigate the effects of using semantic units instead of single words for sentence scoring. We also integrate our scoring metric as an auxiliary feature to a cutting edge summarizer with the intention of examining its effects on the performance. The experiments using datasets from the Document Understanding Conference (DUC) 2004 show that the SRL-based summarization outperforms the term-based approach as well as most of the DUC participants. © 2009 IEEE.EnglishFrequencySemantic role labelingSummarizationCutting edgesData setsMulti-document summarizationSemantic unitsInformation scienceLabelingSemanticsConference Paper10.1109/ISCIS.2009.5291878