10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Hybrids of Supervised and Unsupervised Models for Extractive Speech Summarization

Shih-Hsiang Lin, Yueng-Tien Lo, Yao-Ming Yeh, Berlin Chen

National Taiwan Normal University, Taiwan

Speech summarization, distilling important information and removing redundant and incorrect information from spoken documents, has become an active area of intensive research in the recent past. In this paper, we consider hybrids of supervised and unsupervised models for extractive speech summarization. Moreover, we investigate the use of the unsupervised summarizer to improve the performance of the supervised summarizer when manual labels are not available for training the latter. A novel training data selection and relabeling approach designed to leverage the inter-document or/and the inter-sentence similarity information is explored as well. Encouraging results were initially demonstrated.

Full Paper

Bibliographic reference.  Lin, Shih-Hsiang / Lo, Yueng-Tien / Yeh, Yao-Ming / Chen, Berlin (2009): "Hybrids of supervised and unsupervised models for extractive speech summarization", In INTERSPEECH-2009, 1507-1510.