Effective pseudo-relevance feedback for language modeling in extractive speech summarization

Shih Hung Liu, Kuan Yu Chen, Yu Lun Hsieh, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

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

4 Citations (Scopus)

Abstract

Extractive speech summarization, aiming to automatically select an indicative set of sentences from a spoken document so as to concisely represent the most important aspects of the document, has become an active area for research and experimentation. An emerging stream of work is to employ the language modeling (LM) framework along with the Kullback-Leibler divergence measure for extractive speech summarization, which can perform important sentence selection in an unsupervised manner and has shown preliminary success. This paper presents a continuation of such a general line of research and its main contribution is two-fold. First, by virtue of pseudo-relevance feedback, we explore several effective sentence modeling formulations to enhance the sentence models involved in the LM-based summarization framework. Second, the utilities of our summarization methods and several widely-used methods are analyzed and compared extensively, which demonstrates the effectiveness of our methods.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3226-3230
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 2014 May 42014 May 9

Publication series

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

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period14/5/414/5/9

Keywords

  • Kullback-Leibler divergence
  • Speech summarization
  • language modeling
  • pseudo-relevance feedback

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Liu, S. H., Chen, K. Y., Hsieh, Y. L., Chen, B., Wang, H. M., Yen, H. C., & Hsu, W. L. (2014). Effective pseudo-relevance feedback for language modeling in extractive speech summarization. In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 (pp. 3226-3230). [6854196] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6854196