A comparative study of probabilistic ranking models for spoken document summarization

Shih Hsiang Lin; Yi Ting Chen; Hsin Min Wang; Berlin Chen

doi:10.1109/ICASSP.2008.4518787

A comparative study of probabilistic ranking models for spoken document summarization

Shih Hsiang Lin^*, Yi Ting Chen, Hsin Min Wang, Berlin Chen

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The purpose of extractive document summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we present a comparative study of various supervised and unsupervised probabilistic ranking models for spoken document summarization on the Chinese broadcast news. Moreover, we also investigate the possibility of using unsupervised summarizers to boost the performance of supervised summarizers when manual labels are not available for the training of supervised summarizers. Encouraging results were initially demonstrated.

Original language	English
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	5025-5028
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518787
Publication status	Published - 2008
Event	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States Duration: 2008 Mar 31 → 2008 Apr 4

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/Territory	United States
City	Las Vegas, NV
Period	2008/03/31 → 2008/04/04

Keywords

Extractive summarization
Probabilistic ranking models
Spoken document summarization
Unsupervised summarizers

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2008.4518787

Cite this

Lin, S. H., Chen, Y. T., Wang, H. M., & Chen, B. (2008). A comparative study of probabilistic ranking models for spoken document summarization. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 5025-5028). Article 4518787 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4518787

A comparative study of probabilistic ranking models for spoken document summarization. / Lin, Shih Hsiang; Chen, Yi Ting; Wang, Hsin Min et al.
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 5025-5028 4518787 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, SH, Chen, YT, Wang, HM & Chen, B 2008, A comparative study of probabilistic ranking models for spoken document summarization. in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP., 4518787, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 5025-5028, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Las Vegas, NV, United States, 2008/03/31. https://doi.org/10.1109/ICASSP.2008.4518787

Lin SH, Chen YT, Wang HM, Chen B. A comparative study of probabilistic ranking models for spoken document summarization. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 5025-5028. 4518787. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2008.4518787

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abstract = "The purpose of extractive document summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we present a comparative study of various supervised and unsupervised probabilistic ranking models for spoken document summarization on the Chinese broadcast news. Moreover, we also investigate the possibility of using unsupervised summarizers to boost the performance of supervised summarizers when manual labels are not available for the training of supervised summarizers. Encouraging results were initially demonstrated.",

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A comparative study of probabilistic ranking models for spoken document summarization

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Keywords

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