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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages377-381
Number of pages5
ISBN (Electronic)9781450336031
DOIs
Publication statusPublished - 2016 Oct 1
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 2016 Oct 152016 Oct 19

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Other

Other24th ACM Multimedia Conference, MM 2016
CountryUnited Kingdom
CityAmsterdam
Period16/10/1516/10/19

Keywords

  • Interpretation
  • Language model
  • Representation
  • Speech summarization
  • Word embedding

ASJC Scopus subject areas

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

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  • Cite this

    Chen, K. Y., Liu, S. H., Chen, B., Wang, H. M., & Chen, H. H. (2016). Novel word embedding and translation-based language modeling for extractive speech summarization. In MM 2016 - Proceedings of the 2016 ACM Multimedia Conference (pp. 377-381). (MM 2016 - Proceedings of the 2016 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2964284.2967246