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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