Entry Wang:2012:TUF from talip.bib

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BibTeX entry

@Article{Wang:2012:TUF,
  author =       "Hongling Wang and Guodong Zhou",
  title =        "Toward a Unified Framework for Standard and Update
                 Multi-Document Summarization",
  journal =      j-TALIP,
  volume =       "11",
  number =       "2",
  pages =        "5:1--5:??",
  month =        jun,
  year =         "2012",
  DOI =          "https://doi.org/10.1145/2184436.2184438",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Tue Jun 12 11:20:16 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "This article presents a unified framework for
                 extracting standard and update summaries from a set of
                 documents. In particular, a topic modeling approach is
                 employed for salience determination and a dynamic
                 modeling approach is proposed for redundancy control.
                 In the topic modeling approach for salience
                 determination, we represent various kinds of text
                 units, such as word, sentence, document, documents, and
                 summary, using a single vector space model via their
                 corresponding probability distributions over the
                 inherent topics of given documents or a related corpus.
                 Therefore, we are able to calculate the similarity
                 between any two text units via their topic probability
                 distributions. In the dynamic modeling approach for
                 redundancy control, we consider the similarity between
                 the summary and the given documents, and the similarity
                 between the sentence and the summary, besides the
                 similarity between the sentence and the given
                 documents, for standard summarization while for update
                 summarization, we also consider the similarity between
                 the sentence and the history documents or summary.
                 Evaluation on TAC 2008 and 2009 in English language
                 shows encouraging results, especially the dynamic
                 modeling approach in removing the redundancy in the
                 given documents. Finally, we extend the framework to
                 Chinese multi-document summarization and experiments
                 show the effectiveness of our framework.",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing (TALIP)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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