Sentence modeling for extractive speech summarization

Berlin Chen, Hao Chin Chang, Kuan Yu Chen

研究成果: 書貢獻/報告類型會議論文篇章

13 引文 斯高帕斯(Scopus)


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.

主出版物標題2013 IEEE International Conference on Multimedia and Expo, ICME 2013
出版狀態已發佈 - 2013
事件2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, 美国
持續時間: 2013 7月 152013 7月 19


名字Proceedings - IEEE International Conference on Multimedia and Expo


其他2013 IEEE International Conference on Multimedia and Expo, ICME 2013
城市San Jose, CA

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用


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