Trainable, scalable summarization using robust NLP and machine learning

  • Aone C
  • Okurowski M
  • Gorlinsky J
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Abstract

We describe a trainable and scalable sum-marization system which utilizes features derived from information retrieval, infor-mation extraction, and NLP techniques and on-line resources. The system com-bines these features using a trainable fea-ture combiner learned from summary ex-amples through a machine learning algo-rithm. We demonstrate system scalability by reporting results on the best combina-tion of summarization features for different document sources. We also present prelim-inary results from a task-based evaluation on summarization output usability.

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Authors

  • Chinatsu Aone

  • Mary Ellen Okurowski

  • James Gorlinsky

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