Trainable, scalable summarization using robust NLP and machine learning

Authors:
Chinatsu Aone;Mary Ellen Okurowski;James Gorlinsky
Affiliations:
SRA International, Fairfax, VA;Department of Defense, Fort Meade, MD;SRA International, Fairfax, VA
Venue:
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Year:
1998

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Abstract

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 from a task-based evaluation on summarization output usability.