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.
|頁（從 - 到）||1507-1510|
|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 6 → 2009 九月 10
ASJC Scopus subject areas