Text Summarizers for Education News Articles
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 4
Abstract
Text summarization is a powerful text mining technique for condensing the contents of the documents without loss of context and information. As the text summarization models are highly adopted in areas like natural language processing, information retrieval, text compression, email thread summarization, library sciences there is necessity for building innovative text summarization models. In this research work, the text summarization models have been built using TextRank algorithm, though algorithm like LexRank, LSA have been used earlier. New dataset has been developed to test the performance of TextRank based text summarization model. The model is compared with LexRank and LSA based text summarization models and opinosis datasets. ROUGE scores such as ROUGE 1, ROUGE 2, ROUGE L which includes precision, recall, Fmeasure along with cosine similarity and relative utility are used as metrics for performance evaluation. The comparative analysis of all three summarizers shows that Text rank algorithm performs better for education dataset than other two algorithms.
Authors and Affiliations
Vishnu Preethi K, Vijaya MS
Keywords
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