Name
Abstract | ||
|---|---|---|
Due to a large variety of noisy information embedded in Web pages, Web-page classification is much more difficult than pure-text classification. In this paper, we propose to improve the Web-page classification performance by removing the noise through summarization techniques. 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 put forward a new Web-page summarization algorithm based on Web-page layout and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that the classification algorithms (NB or SVM) augmented by any summarization approach can achieve an improvement by more than 5.0% as compared to pure-text-based classification algorithms. We further introduce an ensemble method to combine the different summarization algorithms. The ensemble summarization method achieves more than 12.0% improvement over pure-text based methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ipm.2007.01.013 | Inf. Process. Manage. |
Keywords | Field | DocType |
new web-page summarization algorithm,web-page classification,content body,pure-text classification,classification algorithm,web-page layout,ensemble summarization method,noise reduction,web-page summarization,web-page categorization,web-page classification algorithm,web-page classification performance,ideal web-page summary,different summarization algorithm,empirical evidence,text summarization,web pages | Noise reduction,Data mining,Automatic summarization,Information retrieval,Web page,Computer science,Support vector machine,Information extraction,Automatic processing,Statistical classification,Web directory | Journal |
Volume | Issue | ISSN |
43 | 6 | Information Processing and Management |
Citations | PageRank | References |
12 | 0.76 | 30 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dou Shen | 1 | 1224 | 59.46 |
| Qiang Yang | 2 | 17039 | 875.69 |
| Zheng Chen | 3 | 5019 | 256.89 |