A Hierarchical Neural Summarization Framework for Spoken Documents

Tzu En Liu, Shih Hung Liu, Berlin Chen

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

3 Citations (Scopus)

Abstract

Extractive text or speech summarization seeks to select indicative sentences from a source document and assemble them together to form a succinct summary, so as to help people to browse and understand the main theme of the document efficiently. A more recent trend is towards developing supervised deep learning based methods for extractive summarization. This paper extends and contextualizes this line of research for spoken document summarization, while its contributions are at least three-fold. First, we propose a neural summarization framework with the flexibility to incorporate extra acoustic/prosodic and lexical features, for which the ROUGE evaluation metric is embedded into the training objective function and can be optimized with reinforcement learning. Second, disparate ways to integrate acoustic features into this framework are investigated. Third, the utility of our proposed summarization methods and several widely-used state-of-the-art ones are extensively compared and evaluated. A series of empirical experiments seem to demonstrate the effectiveness of our summarization methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7185-7189
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 2019 May
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 2019 May 122019 May 17

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period19/5/1219/5/17

Keywords

  • Extractive spoken document summarization
  • convolutional neural network
  • hierarchical encoding
  • recurrent neural network
  • reinforcement learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Liu, T. E., Liu, S. H., & Chen, B. (2019). A Hierarchical Neural Summarization Framework for Spoken Documents. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 7185-7189). [8683758] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683758