We automatically extract the summaries of spoken class lectures. This paper presents a novel method for sentence extraction-based automatic speech summarization.
We propose a technique that extracts "cue phrases for important sentences (CPs)" that often appear in important sentences. We formulate CP extraction as a labeling problem of word sequences and use Conditional Random Fields (CRF)  for labeling. Automatic summarization using CP extraction results as features yields precisions of 0.603 and 0.556 when using manual transcriptions and Automatic Speech Recognition (ASR) results, respectively.
Combining the features derived from the CPs and traditional features (including repeated words, words repeated in a slide text, and term frequency (tf), which are surface linguistic information, and speech power and duration, which are prosodic features) [2, 3], we obtained better summarization performance with a κ-value of 0.380, a F-measure of 0.539, and a Rouge-4 of 0.709.
Bibliographic reference. Fujii, Yasuhisa / Kitaoka, Norihide / Nakagawa, Seiichi (2007): "Automatic extraction of cue phrases for important sentences in lecture speech and automatic lecture speech summarization", In INTERSPEECH-2007, 2801-2804.