Learning deep rhetorical structure for extractive speech summarization

Extractive summarization of conference and lecture speech is useful for online learning and references. We show for the first time that deep(er) rhetorical parsing of conference speech is possible and helpful to extractive summarization task. This type of rhetorical structures is evident in the corresponding presentation slide structures. We propose using Hidden Markov SVM (HMSVM) to iteratively learn the rhetorical structure of the speeches and summarize them. We show that system based on HMSVM gives a 64.3% ROUGE-L F-measure, a 10.1% absolute increase in lecture speech summarization performance compared with the baseline system without rhetorical information. Our method equally outperforms the baseline with a conventional discourse feature. Our proposed approach is more efficient than and also improves upon a previous method of using shallow rhetorical structure parsing [1].

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Justin Jian Zhang, Pascale Fung
• Keywords: Extractive Summarization, ICASSP 2010, Lecture Speech, Rhetorical Structure, Signal Processing