Hybrids of supervised and unsupervised models for extractive speech summarization

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

研究成果: 雜誌貢獻會議論文同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)1507-1510
頁數4
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態已發佈 - 2009 十一月 26
事件10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, 英国
持續時間: 2009 九月 62009 九月 10

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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