Spoken document summarization using relevant information

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

研究成果: 會議貢獻類型

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁面189-194
頁數6
出版狀態已發佈 - 2007 十二月 1
事件2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007 - Kyoto, 日本
持續時間: 2007 十二月 92007 十二月 13

其他

其他2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007
國家日本
城市Kyoto
期間07/12/907/12/13

    指紋

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software
  • Artificial Intelligence

引用此

Chen, Y. T., Lin, S. H., Wang, H. M., & Chen, B. (2007). Spoken document summarization using relevant information. 189-194. 論文發表於 2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007, Kyoto, 日本.