Novel word embedding and translation-based language modeling for extractive speech summarization

Kuan Yu Chen, Shih Hung Liu, Berlin Chen, Hsin Min Wang, Hsin Hsi Chen

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in turn be used to induce similarity measures among words, sentences and documents in context. Celebrated methods can be categorized as prediction-based and count-based methods according to the training objectives and model architectures. Their pros and cons have been extensively analyzed and evaluated in recent studies, but there is relatively less work continuing the line of research to develop an enhanced learning method that brings together the advantages of the two model families. In addition, the interpretation of the learned word representations still remains somewhat opaque. Motivated by the observations and considering the pressing need, this paper presents a novel method for learning the word representations, which not only inherits the advantages of classic word embedding methods but also offers a clearer and more rigorous interpretation of the learned word representations. Built upon the proposed word embedding method, we further formulate a translation-based language modeling framework for the extractive speech summarization task. A series of empirical evaluations demonstrate the effectiveness of the proposed word representation learning and language modeling techniques in extractive speech summarization.

原文英語
主出版物標題MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
發行者Association for Computing Machinery, Inc
頁面377-381
頁數5
ISBN(電子)9781450336031
DOIs
出版狀態已發佈 - 2016 十月 1
事件24th ACM Multimedia Conference, MM 2016 - Amsterdam, 英国
持續時間: 2016 十月 152016 十月 19

出版系列

名字MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

其他

其他24th ACM Multimedia Conference, MM 2016
國家英国
城市Amsterdam
期間2016/10/152016/10/19

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Software

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