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2008
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A comparative study of probabilistic ranking models for spoken document summarization

9 years 4 months ago
A comparative study of probabilistic ranking models for spoken document summarization
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.
Shih-Hsiang Lin, Yi-Ting Chen, Hsin-Min Wang, Bin
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Shih-Hsiang Lin, Yi-Ting Chen, Hsin-Min Wang, Bin Chen
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