Table based Version of K Nearest Neighbor used for Text Summarization

This article proposes the modified KNN (K Nearest Neighbor)algorithm which receives a table as its input data and is applied tothe text summarization. The table based algorithms workedsuccessfully in text mining tasks such as text categorization andtext clustering in previous works, and the text summarization isable to be viewed into a binary classification where each paragraphis classified into summary or non-summary. In the proposed system, atext which is given as the input is partitioned into a list ofparagraphs, each paragraph is classified by the proposed KNNversion, and the paragraphs which are classified into summary areextracted ad the output. The proposed KNN version is empiricallyvalidated as the better approach in deciding whether each paragraphis essential or not in news articles and opinions. In using thetable based KNN algorithm, it is easier to trace results fromcategorizing paragraphs.