Entry Lin:2009:CSP from talip.bib
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BibTeX entry
@Article{Lin:2009:CSP,
author = "Shih-Hsiang Lin and Berlin Chen and Hsin-Min Wang",
title = "A Comparative Study of Probabilistic Ranking Models
for {Chinese} Spoken Document Summarization",
journal = j-TALIP,
volume = "8",
number = "1",
pages = "3:1--3:??",
month = mar,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1145/1482343.1482346",
ISSN = "1530-0226 (print), 1558-3430 (electronic)",
ISSN-L = "1530-0226",
bibdate = "Mon Mar 23 16:32:22 MDT 2009",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/talip.bib",
abstract = "Extractive document summarization automatically
selects a number of indicative sentences, passages, or
paragraphs from an original document according to a
target summarization ratio, and sequences them to form
a concise summary. In this article, we present a
comparative study of various probabilistic ranking
models for spoken document summarization, including
supervised classification-based summarizers and
unsupervised probabilistic generative summarizers. We
also investigate the use of unsupervised summarizers to
improve the performance of supervised summarizers when
manual labels are not available for training the
latter. A novel training data selection approach that
leverages the relevance information of spoken sentences
to select reliable document-summary pairs derived by
the probabilistic generative summarizers is explored
for training the classification-based summarizers.
Encouraging initial results on Mandarin Chinese
broadcast news data are demonstrated.",
acknowledgement = ack-nhfb,
articleno = "3",
fjournal = "ACM Transactions on Asian Language Information
Processing",
journal-URL = "http://portal.acm.org/browse_dl.cfm?&idx=J820",
keywords = "extractive summarization; probabilistic ranking
models; relevance information; spoken document
summarization",
}
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