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Text Summarization with KNN Algorithm by Table Similarity

Shakshi Neha, Arun Kumar Yadav


This paper proposes Text Summarization using K Nearest Neighbor Classification algorithm on the basis of table similarity. This is being proposed for blog summarization, so that the text summarization can be used for summarization of blogs. Today, summarized data is needed everywhere, so that people can get more information in less amount of time, and that can be given with the help of summarized blogs. This method is being used for changing encoding writings into numerical vectors for utilizing the customary variants for content mining assignments causing the three primary issues: the enormous dimensionality, the inadequate appropriation, and the poor content quality. The possibility of this exploration is to decipher the content rundown task into the content grouping task and apply the proposed adaptation of KNN to the undertaking where writings are encoded into tables. The adjusted rendition which is proposed right now expected to outline messages all the more dependably than the conventional form by taking care of the three issues. Thus, the objective of this exploration is to execute the content rundown framework, utilizing the proposed approach.

Keywords: Blog summarization, encoding, KNN, table similarity, text summarization

Cite this Article: Shakshi Neha, Arun Kumar Yadav. Text Summarization with KNN Algorithm by Table Similarity. Journal of Multimedia Technology & Recent Advancements. 2020; 7(1): 14–18p.

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