A risk-aware modeling framework for speech summarization

Berlin Chen*, Shih Hsiang Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (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.

Original languageEnglish
Article number5876303
Pages (from-to)211-222
Number of pages12
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number1
Publication statusPublished - 2012


  • Decision-making
  • language modeling
  • loss functions
  • risk minimization
  • speech summarization

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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