%0 Conference Paper
%1 Shen:2004:WCT:1008992.1009035
%A Shen, Dou
%A Chen, Zheng
%A Yang, Qiang
%A Zeng, Hua-Jun
%A Zhang, Benyu
%A Lu, Yuchang
%A Ma, Wei-Ying
%B Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
%C New York, NY, USA
%D 2004
%I ACM
%K classification web bachelor:2011:bachmann
%P 242--249
%T Web-page classification through summarization
%U http://doi.acm.org/10.1145/1008992.1009035
%X Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.