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We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, information extraction, and NLP techniques and on-line resources. The system combines these features using a trainable feature combiner learned from summary examples through a machine learning algorithm. We demonstrate system scalability by reporting results on the best combination of summarization features for different document sources. We also present preliminary results fromdoi:10.3115/980845.980856 dblp:conf/acl/AoneOG98 fatcat:hillyrwqqjccriasufi3lbxe5q