Spoken document summarization using relevant information

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

*此作品的通信作者

研究成果: 會議貢獻類型會議論文同行評審

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/地區日本
城市Kyoto
期間2007/12/092007/12/13

ASJC Scopus subject areas

  • 電腦視覺和模式識別
  • 軟體
  • 人工智慧

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