Hybrids of supervised and unsupervised models for extractive speech summarization

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

Research output: Contribution to journalConference article

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 languageEnglish
Pages (from-to)1507-1510
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2009 Nov 26
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 2009 Sep 62009 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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