Constructing effective ranking models for speech summarization

Yueng Tien Lo, Shih Hsiang Lin, Berlin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Speech summarization, facilitating users to better browse through and understand speech information (especially, spoken documents), has become an active area of intensive research recently. Many of the existing machine-learning approaches to speech summarization cast important sentence selection as a two-class classification problem and have shown empirical success for a wide array of summarization tasks. One common deficiency of these approaches is that the corresponding learning criteria are loosely related to the final evaluation metric. To cater for this problem, we present a novel probabilistic framework to learn the summarization models, building on top of the Bayes decision theory. Two effective training criteria, viz. maximum relevance estimation (MRE) and minimum ranking loss estimation (MRLE), deduced from such a framework are introduced to characterize the pair-wise preference relationships between spoken sentences. Experiments on a broadcast news speech summarization task exhibit the performance merits of our summarization methods when compared to existing methods.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages5053-5056
Number of pages4
DOIs
Publication statusPublished - 2012 Oct 23
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 2012 Mar 252012 Mar 30

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period12/3/2512/3/30

    Fingerprint

Keywords

  • evaluation metric
  • imbalanced-data
  • ranking capability
  • sentence-classification
  • speech summarization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Lo, Y. T., Lin, S. H., & Chen, B. (2012). Constructing effective ranking models for speech summarization. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 5053-5056). [6289056] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6289056