Automatic indexing of key sentences for lecture archives using statistics of presumed discourse markers

Automatic extraction of key sentences from lecture audio archives is addressed. The method makes use of the characteristic expressions used in initial utterances of sections, which are defined as discourse markers and derived in a totally unsupervised manner based on word statistics. The statistics of the presumed discourse markers are then used to define the importance of the sentences. It is also combined with the conventional tf-idf measure of content words. Experimental results using a large corpus of lectures confirm the effectiveness of the method based on the discourse markers and its combination with the keyword-based method. It is also shown that the method is robust against ASR errors and sentence segmentation accuracy is more vital. Thus, we also enhance segmentation by incorporating prosodic information.