Improving lecture speech summarization using rhetorical information

We propose a novel method of extractive summarization of lecture speech based on unsupervised learning of its rhetorical structure. We present empirical evidence showing that rhetorical structure is the underlying semantics which is then rendered in linguistic and acoustic/prosodic forms in lecture speech. We present a first thorough investigation of the relative contribution of linguistic versus acoustic features and show that, at least for lecture speech, what is said is more important than how it is said. We base our experiments on conference speeches and corresponding presentation slides as the latter is a faithful description of the rhetorical structure of the former. We find that discourse features from broadcast news are not applicable to lecture speech. By using rhetorical structure information in our summarizer, its performance reaches 67.87% ROUGE-L F-measure at 30% compression, surpassing all previously reported results. The performance is also superior to the 66.47% ROUGE-L F-measure of baseline summarization performance without rhetorical information. We also show that, despite a 29.7% character error rate in speech recognition, extractive summarization performs relatively well, underlining the fact that spontaneity in lecture speech does not affect the central meaning of lecture speech.