A risk-aware modeling framework for speech summarization

Berlin Chen*, Shih Hsiang Lin


研究成果: 雜誌貢獻期刊論文同行評審

17 引文 斯高帕斯(Scopus)


Extractive speech summarization attempts to select a representative set of sentences from a spoken document so as to succinctly describe the main theme of the original document. In this paper, we adapt the notion of risk minimization for extractive speech summarization by formulating the selection of summary sentences as a decision-making problem. To this end, we develop several selection strategies and modeling paradigms that can leverage supervised and unsupervised summarization models to inherit their individual merits as well as to overcome their inherent limitations. On top of that, various component models are introduced, providing a principled way to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. A series of experiments on speech summarization seem to demonstrate that the methods deduced from our summarization framework are very competitive with existing summarization methods.

頁(從 - 到)211-222
期刊IEEE Transactions on Audio, Speech and Language Processing
出版狀態已發佈 - 2012

ASJC Scopus subject areas

  • 聲學與超音波
  • 電氣與電子工程


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