Zhuli Xie, Barbara Di Eugenio, Peter C. Nelson
In this paper, we propose a novel framework for extractive summarization. Our framework allows the summarizer to adapt and improve itself. Experimental results show that our summarizer achieves higher evaluation scores by adapting to the given evaluation metrics.
Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery
Submitted: Feb 10, 2006