Automatic Summarization of Highly Spontaneous Speech

This paper addresses speech summarization of highly spontaneous speech. Speech is converted into text using an ASR, then segmented into tokens. Human made and automatic, prosody based tokenization are compared. The obtained sentence-like units are analysed by a syntactic parser to help automatic sentence selection for the summary. The preprocessed sentences are ranked based on thematic terms and sentence position. The thematic term is expressed in two ways: TF-IDF and Latent Semantic Indexing. The sentence score is calculated as linear combination of the thematic term score and a sentence position score. To generate the summary, the top 10 candidates for the most informative/best summarizing sentences are selected. The system performance showed comparable results (recall: 0.62, precision: 0.79 and F-measure 0.68) with the prosody based tokenization approach. A subjective test is also carried out on a Likert scale.