Trainable, Scalable Summarization Using Robust NLP and Machine Learning

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.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Chinatsu Aone, Mary Ellen Okurowski, James Gorlinsky
• Keywords: ACL 1998, ACL 2007, Scalable Summarization, Summarization Output Usability, Trainable Feature Combiner