Title page for etd-1018107-100507


[Back to Results | New Search]

URN etd-1018107-100507
Author Li-An Yuan
Author's Email Address smokingbug72@hotmail.com
Statistics This thesis had been viewed 5559 times. Download 4577 times.
Department Information Management
Year 2007
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A hybrid approach to automatic text summarization
Date of Defense 2007-09-28
Page Count 53
Keyword
  • automatic text summarization
  • statistical approach
  • linguistic approach
  • Abstract Automatic text summarization can efficiently and effectively save users’ time while reading text documents. The objective of automatic text summarization is to extract essential sentences that cover almost all the concepts of a document so that
    users are able to comprehend the ideas the document tries to address by simply reading through the corresponding summary. This research focuses on developing a hybrid automatic text summarization
    approach, KCS, to enhancing the quality of summaries.
       This approach basically consists of two major components: first, it employs the K-mixture probabilistic model to calculate term weights in a statistical sense; it then identifies the term relationship
    between nouns and nouns as well as nouns and verbs, which results in the connective strength (CS) of nouns. With the connective strengths available scores of sentences can be calculated and ranked to be extracted.
      We conduct three experiments to justify the proposed approach. The quality of summary is examined by its capability of increasing accuracy of text classification,while the classifier employed, the Naïve Bayes classifier, is kept the same through all experiments. The results show that the K-mixture model is more contributive to document classification than traditional TFIDF weighting scheme. It, however, is still no better than CS, a more complex linguistic-based approach. More importantly, our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.
    Advisory Committee
  • Hsiao Wen Feng - chair
  • Sun Pei Chen - co-chair
  • Chang Te Min - advisor
  • Files
  • etd-1018107-100507.pdf
  • indicate access worldwide
    Date of Submission 2007-10-18

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys