Automatic text summarization using string vector based K nearest neighbor

Issue title: Special Section: Green and Human Information Technology

Guest editors: Seong Oun Hwang

Article type: Research Article

Authors: Jo, Taeho^{; *}

Affiliations: School of Game, Hongik University, 2639, Sejongro, Sejong, South Korea

Correspondence: [*] Corresponding author. Taeho Jo, School of Game, Hongik University, 2639, Sejongro Sejong, South Korea. E-mail: tjo018@hongik.ac.kr.

Abstract: This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a string vector as its input data and is applied to the text summarization. The results from applying the string vector based algorithms to the text categorizations were successful in previous works and the text summarization is able to be viewed into a binary classification where each paragraph is classified into summary or non-summary. In the proposed system, a text which is given as the input is partitioned into a list of paragraphs, each paragraph is classified by the proposed KNN version, and the paragraphs which are classified into summary are extracted ad the output. The proposed KNN version is empirically validated as the better approach in deciding whether each paragraph is essential or not in news articles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying more advanced machine learning algorithms.

Keywords: String vector, semantic similarity, string vector based KNN, text summarization

DOI: 10.3233/JIFS-169841

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6005-6016, 2018