Incorporating proximity information in relevance language modeling for extractive speech summarization

Shih Hung Liu, Hung Shih Lee, Hsiao Tsung Hung, Kuan Yu Chen, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議論文篇章

3 引文 斯高帕斯(Scopus)

摘要

Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.

原文英語
主出版物標題2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
發行者Institute of Electrical and Electronics Engineers Inc.
頁面401-407
頁數7
ISBN(電子)9789881476807
DOIs
出版狀態已發佈 - 2016 二月 19
事件2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, 香港
持續時間: 2015 十二月 162015 十二月 19

出版系列

名字2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

其他

其他2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
國家/地區香港
城市Hong Kong
期間2015/12/162015/12/19

ASJC Scopus subject areas

  • 人工智慧
  • 建模與模擬
  • 訊號處理

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