ABSTRACT\nIn today�s digital era, it becomes a challenge for netizens to find\nspecific information on the internet. Many web-based documents\nare retrieved and it is not easy to digest all the retrieved\ninformation. Automatic text summarization is a process that\nidentifies the important points from all the related documents to\nproduce a concise summary. In this paper, we propose a text\nsummarization model based on classification using neuro-fuzzy\napproach. The model can be trained to filter high-quality\nsummary sentences. We then compare the performance of our\nproposed model with the existing approaches, which are based\non fuzzy logic and neural network techniques. ANFIS showed\nimproved results compared to the previous techniques in terms of\naverage precision, recall and F-measure on the Document\nUnderstanding Conference (DUC) data corpus.
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