Spoken document summarization using relevant information

Yi Ting Chen*, Shih Hsiang Lin, Hsin Min Wang, Berlin Chen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


Extractive summarization usually automatically selects indicative sentences from a document according to a certain target summarization ratio, and then sequences them to form a summary. In this paper, we investigate the use of information from relevant documents retrieved from a contemporary text collection for each sentence of a spoken document to be summarized in a probabilistic generative framework for extractive spoken document summarization. In the proposed methods, the probability of a document being generated by a sentence is modeled by a hidden Markov model (HMM), while the retrieved relevant text documents are used to estimate the HMM's parameters and the sentence's prior probability. The results of experiments on Chinese broadcast news compiled in Taiwan show that the new methods outperform the previous HMM approach.

Original languageEnglish
Number of pages6
Publication statusPublished - 2007
Event2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007 - Kyoto, Japan
Duration: 2007 Dec 92007 Dec 13


Other2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007


  • Extractive summarization
  • Hidden Markov model
  • Probabilistic generative model
  • Relevance model
  • Relevant document
  • Spoken document summarization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software
  • Artificial Intelligence


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