Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization

Shih Hung Liu, Kuan Yu Chen, Yu Lun Hsieh, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

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

Abstract

Extractive summarization systems attempt to automatically pick out representative sentences from a source text or spoken document and concatenate them into a concise summary so as to help people grasp salient information effectively and efficiently. Recent advances in applying nonnegative matrix factorization (NMF) on various tasks including summarization motivate us to extend this line of research and provide the following contributions. First, we propose to employ graph-regularized nonnegative matrix factorization (GNMF), in which an affinity graph with its similarity measure tailored to the evaluation metric of summarization is constructed and in turn serves as a neighborhood preserving constraint of NMF, so as to better represent the semantic space of sentences in the document to be summarized. Second, we further consider sparsity and orthogonality constraints on NMF and GNMF for better selection of representative sentences to form a summary. Extensive experiments conducted on a Mandarin broadcast news speech dataset demonstrate the effectiveness of the proposed unsupervised summarization models, in relation to several widely-used state-of-the-art methods compared in the paper.

Original languageEnglish
Title of host publication2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789881476821
DOIs
Publication statusPublished - 2017 Jan 17
Event2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
Duration: 2016 Dec 132016 Dec 16

Publication series

Name2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
CountryKorea, Republic of
CityJeju
Period16/12/1316/12/16

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Factorization
Semantics
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

Cite this

Liu, S. H., Chen, K. Y., Hsieh, Y. L., Chen, B., Wang, H. M., Yen, H. C., & Hsu, W. L. (2017). Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820883] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820883

Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. / Liu, Shih Hung; Chen, Kuan Yu; Hsieh, Yu Lun; Chen, Berlin; Wang, Hsin Min; Yen, Hsu Chun; Hsu, Wen Lian.

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820883 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

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

Liu, SH, Chen, KY, Hsieh, YL, Chen, B, Wang, HM, Yen, HC & Hsu, WL 2017, Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820883, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820883
Liu SH, Chen KY, Hsieh YL, Chen B, Wang HM, Yen HC et al. Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820883. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). https://doi.org/10.1109/APSIPA.2016.7820883
Liu, Shih Hung ; Chen, Kuan Yu ; Hsieh, Yu Lun ; Chen, Berlin ; Wang, Hsin Min ; Yen, Hsu Chun ; Hsu, Wen Lian. / Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).
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