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ICASSP
2010
IEEE

Learning deep rhetorical structure for extractive speech summarization

12 years 1 months ago
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].
Justin Jian Zhang, Pascale Fung
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Justin Jian Zhang, Pascale Fung
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