Hybrids of supervised and unsupervised models for extractive speech summarization

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

Hybrids of supervised and unsupervised models for extractive speech summarization

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

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

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

Keywords

Hybrid summarizer
Speech summarization
Unsupervised training

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing
Software
Sensory Systems

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AU - Lin, Shih Hsiang

AU - Lo, Yueng Tien

AU - Yeh, Yao Ming

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Y1 - 2009/11/26

N2 - 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.

AB - 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.

KW - Hybrid summarizer

KW - Speech summarization

KW - Unsupervised training

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JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

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Y2 - 6 September 2009 through 10 September 2009

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Hybrids of supervised and unsupervised models for extractive speech summarization

Abstract

Keywords

ASJC Scopus subject areas

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