Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1507-1510 |
| Number of pages | 4 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Publication status | Published - 2009 Nov 26 |
| Event | 10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom Duration: 2009 Sep 6 → 2009 Sep 10 |
Fingerprint
Keywords
- Hybrid summarizer
- Speech summarization
- Unsupervised training
ASJC Scopus subject areas
- Human-Computer Interaction
- Signal Processing
- Software
- Sensory Systems
Cite this
Lin, S. H., Lo, Y. T., Yeh, Y. M., & Chen, B. (2009). Hybrids of supervised and unsupervised models for extractive speech summarization. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 1507-1510.