Abstract
Text classification categorizes web documents in large collections into predefined classes based on their contents. Unfortunately, the classification process can be time-consuming and users are still required to spend considerable amount of time scanning through the classified web documents to identify the ones with contents that satisfy their information needs. In solving this problem, we first introduce CorSum, an extractive single-document summarization approach, which is simple and effective in performing the summarization task, since it only relies on word similarity to generate high-quality summaries. We further enhance CorSum by considering the significance factor of sentences in documents, in addition to using word-correlation factors, for document summarization. We denote the enhanced approach CorSum-SF and use the summaries generated by CorSum-SF to train a Multinomial Naïve Bayes classifier for categorizing web document summaries into predefined classes. Experimental results on the DUC-2002 and 20 Newsgroups datasets show that CorSum-SF outperforms other extractive summarization methods, and classification time (accuracy, respectively) is significantly reduced (compatible, respectively) using CorSum-SF generated summaries compared with using the entire documents. More importantly, browsing summaries, instead of entire documents, which are assigned to predefined categories, facilitates the information search process on the Web.
Original language | English |
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Pages (from-to) | 465-486 |
Number of pages | 22 |
Journal | International Journal on Artificial Intelligence Tools |
Volume | 19 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |
Keywords
- Multinomial Naïve Bayes classifier
- sentence-based summaries
- significant factors
- word correlation