Text Summarization Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Pooya Khosraviyan Dehkordi and Farshad Kumarci and 
                 Hamid Khosravi",
  title =        "Text Summarization Based on Genetic Programming",
  year =         "2013",
  month =        oct # "~30",
  number =       "1/",
  keywords =     "genetic algorithms, genetic programming, automatic
                 text summarisation, vectorial model",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  issue =        "57-64",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  broken =       "http://ijcir.org/volume3-number1/issue 57-64.pdf",
  abstract =     "This work proposes an approach to address the problem
                 of improving content selection in automatic text
                 summarisation by using some statistical tools. This
                 approach is a trainable summariser, which takes into
                 account several features, for each sentence to generate
                 summaries. First, we investigate the effect of each
                 sentence feature on the summarization task. Then we use
                 all features in combination to train genetic
                 programming (GP), vector approach and fuzzy approach in
                 order to construct a text summariser for each model.
                 Furthermore, we use trained models to test
                 summarisation performance. The proposed approach
                 performance is measured at several compression rates on
                 a data corpus composed of 17 English scientific

Genetic Programming entries for Pooya Khosraviyan Dehkordi Farshad Kumarci Hamid Khosravi