%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 %R 10.1145/1008992.1009035 %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. %@ 1-58113-881-4

@inproceedings{Shen:2004:WCT:1008992.1009035, abstract = {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.}, acmid = {1009035}, added-at = {2011-11-21T11:02:57.000+0100}, address = {New York, NY, USA}, author = {Shen, Dou and Chen, Zheng and Yang, Qiang and Zeng, Hua-Jun and Zhang, Benyu and Lu, Yuchang and Ma, Wei-Ying}, biburl = {https://www.bibsonomy.org/bibtex/2b83fca9d43e5afdea78b9791cc07890c/dbenz}, booktitle = {Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval}, description = {Web-page classification through summarization}, doi = {10.1145/1008992.1009035}, interhash = {328ff5b51cb573cd1d253f339892c029}, intrahash = {b83fca9d43e5afdea78b9791cc07890c}, isbn = {1-58113-881-4}, keywords = {classification web bachelor:2011:bachmann}, location = {Sheffield, United Kingdom}, numpages = {8}, pages = {242--249}, publisher = {ACM}, series = {SIGIR '04}, timestamp = {2013-07-31T15:39:42.000+0200}, title = {Web-page classification through summarization}, url = {http://doi.acm.org/10.1145/1008992.1009035}, year = 2004 }