Detecting Human Features in Summaries – Symbol Sequence Statistical Regularity

  • George Giannakopoulos
  • Vangelis Karkaletsis
  • George A. Vouros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)

Abstract

The presented work studies textual summaries, aiming to detect the qualities of human multi-document summaries, in contrast to automatically extracted ones. The measured features are based on a generic statistical regularity measure, named Symbol Sequence Statistical Regularity (SSSR). The measure is calculated over both character and word n-grams of various ranks, given a set of human and automatically extracted multi-document summaries from two different corpora. The results of the experiments indicate that the proposed measure provides enough distinctive power to discriminate between the human and non-human summaries. The results hint on the qualities a human summary holds, increasing intuition related to how a good summary should be generated.

Keywords

Machine Translation Computational Linguistics Property Grammar Input Document Summarization System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • George Giannakopoulos
    • 1
  • Vangelis Karkaletsis
    • 1
  • George A. Vouros
    • 2
  1. 1.Software and Knowledge Engineering LaboratoryNational Center of Scientific Research “Demokritos”Greece
  2. 2.Department of Digital SystemsUniversity of PireausGreece

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