Web-Page Classification Through Summarization

  • Dou Shen ,
  • Zheng Chen ,
  • Qiang Yang ,
  • Hua-Jun Zeng ,
  • Benyu Zhang ,
  • Yuchang Lu ,
  • Wei-Ying Ma

27th annual international ACM SIGIR conference on Research and development in informaion retrieval |

Published by Association for Computing Machinery, Inc.

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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 Webpage 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.