Extractive speech summarization leveraging convolutional neural network techniques

Chun I. Tsai, Hsiao Tsung Hung, Kuan Yu Chen, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

6 引文 斯高帕斯(Scopus)

摘要

Extractive text or speech summarization endeavors to select representative sentences from a source document and assemble them into a concise summary, so as to help people to browse and assimilate the main theme of the document efficiently. The recent past has seen a surge of interest in developing deep learning- or deep neural network-based supervised methods for extractive text summarization. This paper presents a continuation of this line of research for speech summarization and its contributions are three-fold. First, we exploit an effective framework that integrates two convolutional neural networks (CNNs) and a multilayer perceptron (MLP) for summary sentence selection. Specifically, CNNs encode a given document-sentence pair into two discriminative vector embeddings separately, while MLP in turn takes the two embeddings of a document-sentence pair and their similarity measure as the input to induce a ranking score for each sentence. Second, the input of MLP is augmented by a rich set of prosodic and lexical features apart from those derived from CNNs. Third, the utility of our proposed summarization methods and several widely-used methods are extensively analyzed and compared. The empirical results seem to demonstrate the effectiveness of our summarization method in relation to several state-of-the-art methods.

原文英語
主出版物標題2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面158-164
頁數7
ISBN(電子)9781509049035
DOIs
出版狀態已發佈 - 2017 二月 7
事件2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - San Diego, 美国
持續時間: 2016 十二月 132016 十二月 16

出版系列

名字2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings

其他

其他2016 IEEE Workshop on Spoken Language Technology, SLT 2016
國家美国
城市San Diego
期間2016/12/132016/12/16

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence
  • Language and Linguistics
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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