Adaptive Learning in Machine Summarization

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

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