Extractive Based Single Document Text Summarization Using Clustering Approach
Abstract
Text summarization is an old challenge in text mining but in dire need of researcher’s attention in the areas of computational intelligence, machine learning and natural language processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted.
Keywords
K-mean clustering,Stemming,Term Frequency,Text summarization
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PDFDOI: http://doi.org/10.11591/ijai.v3.i2.pp73-78
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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).