Open AccessTEXT SUMMARIZER USING CLUSTERING TECHNIQUES AND ANOMALIES DETECTION ON SVM
Author(s) -
Oyinloye Oghenerukevwe Elohor,
Adesoji susan,
Folake Akinbohun
Publication year - 2019
Publication title -
epra international journal of research and development
Language(s) - English
Resource type - Journals
ISSN - 2455-7838
DOI - 10.36713/epra3758
Subject(s) - automatic summarization , cluster analysis , computer science , support vector machine , data mining , domain (mathematical analysis) , alphanumeric , document clustering , artificial intelligence , information retrieval , mathematics , mathematical analysis , programming language
The study is aimed at developing a text summarizer using clustering and anomalies detection with SVM classification. A text summarization approach is proposed which uses the SVM clustering algorithm. The proposed project can be used to summarize articles from fields as diverse as politics, sports, current affairs, finance and any other explanatory document. However, it does cause a trade-off between domain independence and a knowledge-based summary which would provide data in a form more easily understandable to the user. A bundle of libraries and software’s was utilized for proper text summary of alphanumeric entering.KEYWORDS— Anomalies detection, SVM (support vector machine), clustering, text summarization, data mining