Automatic extraction of cue phrases for important sentences in lecture speech and automatic lecture speech summarization
Yasuhisa Fujii, Norihide Kitaoka, Seiichi Nakagawa · 2007
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 im-portant sentences (CPs) ” that often appear in important sen-tences. We formulate CP extraction as a labeling problem of word sequences and use Conditional Random Fields (CRF) [1] for labeling. Automatic summarization using CP extraction re-sults as features yields precisions of 0.603 and 0.556 when us-ing manual transcriptions and Automatic Speech Recognition (ASR) results, respectively. Combining the features derived from the CPs and tradi-tional 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. Index Terms: automatic speech summarization, sentence ex-traction, speech synthesis