Publication | Open Access
Automatic text summarization using fuzzy inference
42
Citations
12
References
2016
Year
Unknown Venue
EngineeringEntity SummarizationLarge VolumeSemantic WebCorpus LinguisticsText MiningSummarization AccuracyAutomatic SummarizationNatural Language ProcessingInformation RetrievalData ScienceText SummarizationComputational LinguisticsLanguage StudiesContent AnalysisFuzzy LogicKnowledge DiscoveryInformation ExtractionAutomatic Text SummarizationMulti-modal SummarizationKeyword ExtractionLinguistics
Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.
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