Using Signals of Human Interest to Enhance Single-document Summarization

Krysta M. Svore, Lucy Vanderwende, Christopher J.C. Burges

As the amount of information on the Web grows, the ability to retrieve relevant information quickly and easily is necessary. The combination of ample news sources on the Web, little time to browse news, and smaller mobile devices motivates the development of automatic highlight extraction from single news articles. Our system, NetSum, is the first system to produce highlights of an article and significantly outperform the baseline. Our approach uses novel information sources to exploit human interest for highlight extraction. In this paper, we briefly describe the novelties of NetSum, originally presented at EMNLP 2007, and embed our work in the AI context.

Subjects: 13. Natural Language Processing; 14. Neural Networks

Submitted: Apr 10, 2008

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