Incorporating proximity information in relevance language modeling for extractive speech summarization

Shih Hung Liu, Hung Shih Lee, Hsiao Tsung Hung, Kuan Yu Chen, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

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

1 Citation (Scopus)

Abstract

Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.

Original languageEnglish
Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-407
Number of pages7
ISBN (Electronic)9789881476807
DOIs
Publication statusPublished - 2016 Feb 19
Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
Duration: 2015 Dec 162015 Dec 19

Publication series

Name2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Other

Other2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
CountryHong Kong
CityHong Kong
Period15/12/1615/12/19

Fingerprint

Language Modeling
Summarization
Proximity
Pseudo-relevance Feedback
Feedback
Line
Process Modeling
Fold
Covering
Relevance
Speech
Formulation
Framework

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Signal Processing

Cite this

Liu, S. H., Lee, H. S., Hung, H. T., Chen, K. Y., Chen, B., Wang, H. M., ... Hsu, W. L. (2016). Incorporating proximity information in relevance language modeling for extractive speech summarization. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 (pp. 401-407). [7415303] (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2015.7415303

Incorporating proximity information in relevance language modeling for extractive speech summarization. / Liu, Shih Hung; Lee, Hung Shih; Hung, Hsiao Tsung; Chen, Kuan Yu; Chen, Berlin; Wang, Hsin Min; Yen, Hsu Chun; Hsu, Wen Lian.

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 401-407 7415303 (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015).

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

Liu, SH, Lee, HS, Hung, HT, Chen, KY, Chen, B, Wang, HM, Yen, HC & Hsu, WL 2016, Incorporating proximity information in relevance language modeling for extractive speech summarization. in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015., 7415303, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 401-407, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Hong Kong, Hong Kong, 15/12/16. https://doi.org/10.1109/APSIPA.2015.7415303
Liu SH, Lee HS, Hung HT, Chen KY, Chen B, Wang HM et al. Incorporating proximity information in relevance language modeling for extractive speech summarization. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 401-407. 7415303. (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015). https://doi.org/10.1109/APSIPA.2015.7415303
Liu, Shih Hung ; Lee, Hung Shih ; Hung, Hsiao Tsung ; Chen, Kuan Yu ; Chen, Berlin ; Wang, Hsin Min ; Yen, Hsu Chun ; Hsu, Wen Lian. / Incorporating proximity information in relevance language modeling for extractive speech summarization. 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 401-407 (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015).
@inproceedings{4ffaaa0e6b0c4751bb913619fa2e0308,
title = "Incorporating proximity information in relevance language modeling for extractive speech summarization",
abstract = "Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.",
author = "Liu, {Shih Hung} and Lee, {Hung Shih} and Hung, {Hsiao Tsung} and Chen, {Kuan Yu} and Berlin Chen and Wang, {Hsin Min} and Yen, {Hsu Chun} and Hsu, {Wen Lian}",
year = "2016",
month = "2",
day = "19",
doi = "10.1109/APSIPA.2015.7415303",
language = "English",
series = "2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "401--407",
booktitle = "2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015",

}

TY - GEN

T1 - Incorporating proximity information in relevance language modeling for extractive speech summarization

AU - Liu, Shih Hung

AU - Lee, Hung Shih

AU - Hung, Hsiao Tsung

AU - Chen, Kuan Yu

AU - Chen, Berlin

AU - Wang, Hsin Min

AU - Yen, Hsu Chun

AU - Hsu, Wen Lian

PY - 2016/2/19

Y1 - 2016/2/19

N2 - Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.

AB - Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.

UR - http://www.scopus.com/inward/record.url?scp=84986205300&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84986205300&partnerID=8YFLogxK

U2 - 10.1109/APSIPA.2015.7415303

DO - 10.1109/APSIPA.2015.7415303

M3 - Conference contribution

AN - SCOPUS:84986205300

T3 - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

SP - 401

EP - 407

BT - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -