A naïve Bayes Classifier for Web Document Summarie...

480 M. S. Pera & Y.-K. NgFig. 3. ROUGE-N values achieved by different summarization approaches on DUC-2002.since CollabSum approaches yield lower ROUGE-N values than CorSum(-SF) (asshown in Figure 3).Unlike the summarization methods in (i), 21 which requires training the compressionand selection models as a pre-processing step, (ii), 7 which uses a supervisedlearningapproach,and (iii), 2 which learns from a particle swarm optimizationmodel, neither CorSum nor CorSum-SF require any training step for documentsummarization.The summarization methods in Refs. 17 and 18 depend solely on the wordsignificance value of a word w in a sentence S and the word frequency of w, respectively.Contrarily, besides the significance factor of w in S, CorSum-SF usesword-correlation factors to determine the ranking score of S.4.4. Classification performance evaluationWe have evaluated the effectiveness and efficiency of classifying summaries, as opposedto entire documents, using MNB on the 20NG dataset. Figure 4 shows theclassification accuracyachievedby MNB using automatically-generatedsummaries,as well as the entire content, of the documents in 20NG for comparison purpose.Using CorSum generated summaries, MNB achieves a fairly high accuracy, i.e.,