Extractive speech summarization by active learning

In this paper, we propose an active learning approach for feature-based extractive summarization of lecture speech. Most state-of-the-art speech summarization systems are trained by using a large amount of human reference summaries. Active learning targets to minimize human annotation efforts by automatically selecting a small amount of unlabeled examples for labeling. Our method chooses the unlabeled examples according to a combination of informativeness criterion and robustness criterion. Our summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.54, consistently higher than that of using randomly chosen training samples. We also show that, by following the rhetorical structure in presentation slides, it is possible for humans to produce "gold standard" reference summaries with very high inter-labeler agreement.