Automatic genre recognition and adaptive text summarization

@article{Yatsko2010AutomaticGR,
  title={Automatic genre recognition and adaptive text summarization},
  author={Viatcheslav Yatsko and Maxim Starikov and Alexander V. Butakov},
  journal={Automatic Documentation and Mathematical Linguistics},
  year={2010},
  volume={44},
  pages={111-120}
}
This paper describes an experimental method for automatic text genre recognition based on 45 statistical, lexical, syntactic, positional, and discursive parameters. The suggested method includes: (1) the development of software permitting heterogeneous parameters to be normalized and clustered using the k-means algorithm; (2) the verification of parameters; (3) the selection of the parameters that are the most significant for scientific, newspaper, and artistic texts using two-factor analysis… Expand
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References

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Two experiments in automatic genre classification of web pages are presented to highlight three important issues related to genre classification : corpus composition and genre palettes, feature representativeness, and exportability of classification models. Expand
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It is argued that automatic identification of genre in web pages needs more flexible genre classification schemes, and experiments are described that support this claim. Expand
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