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We propose using active learning for tagging extractive reference summary of lecture speech. The training process of feature-based summarization model usually requires a large amount of training data with high-quality reference summaries. Human production of such summaries is tedious, and since inter-labeler agreement is low, very unreliable. Active learning helps assuage this problem by automatically selecting a small amount of unlabeled documents for humans to hand correct. Our method chooses the unlabeled documents according to the similarity score between the document and the comparable resource---PowerPoint slides. After manual correction, the selected documents are returned to the training pool. Summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.514, consistently higher than that of using randomly chosen training samples.

Active learning of extractive reference summaries for lecture speech summarization