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We show a novel approach of parsing and reordering rhetorical syntax tree for extractive summarization of presentation speech. Our previous work showed (Fung et al., 2008) that rhetorical structures are embedded in this type of speech and that exploring this structure helps improve summarization quality. We further demonstrate that speakers do not follow the strict order of bullet points in the presentation slides, and that a re-ordering of these points occurs. We therefore propose a method of parsing presentation transcriptions into a rhetorical syntax tree and then re-order the leaf nodes to transform the speech transcriptions into an extractive summary akin to a process of presentation slide generation. Chunking, parsing, and reordering are carried out by 28-class Hidden Markov Support Vector Machine(HMSVM) classifier trained from reference presentations and presentation slides. Using ROUGE-L F-measure we showed that our rhetorical syntaxdriven model gives a 35.8% relative improvement over a binary summarizer with no rhetorical information, a 14.3% improvement over Rhetorical State Hidden Markov Model(RSHMM) (Fung et al., 2008), and a 4.3% improvement over our proposed model with no reordering.