Sentence modeling for extractive speech summarization

Berlin Chen, Hao Chin Chang, Kuan Yu Chen

研究成果: 書貢獻/報告類型會議貢獻

11 引文 斯高帕斯(Scopus)

摘要

Extractive speech summarization, aiming to select an indicative set of sentences from a spoken document so as to concisely represent the most important aspects of the document, has emerged as an attractive area of research and experimentation. A recent school of thought is to employ the language modeling (LM) framework along with the Kullback-Leibler (KL) divergence measure for important sentence selection, which has shown preliminary promise for extractive speech summarization. Our work in this paper continues this general line of research in two significant aspects. First, we explore a novel sentence modeling approach built on top of the notion of relevance, where the relationship between a candidate summary sentence and the spoken document to be summarized is discovered through various granularities of context for relevance modeling. Second, not only lexical but also topical cues inherent in the spoken document are exploited for sentence modeling. Experiments on broadcast news summarization seem to demonstrate the performance merits of our methods when compared to several existing methods.

原文英語
主出版物標題2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
出版狀態已發佈 - 2013 十月 21
事件2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, 美国
持續時間: 2013 七月 152013 七月 19

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(列印)1945-7871
ISSN(電子)1945-788X

其他

其他2013 IEEE International Conference on Multimedia and Expo, ICME 2013
國家美国
城市San Jose, CA
期間13/7/1513/7/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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