Artificial Intelligence

    

Summarizing Texts Automatically by Graph based Version of K Nearest Neighbor

Authors: Taeho Jo

This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a graph as its input data and is applied tothe text summarization. The graph is more graphical for representing a word and the text summarization is able to be viewed into a binaryclassification where each paragraph is classified into summary or non-summary. In the proposed system, a text which is given as theinput is partitioned into a list of paragraphs, each paragraph is classified by the proposed KNN version, and the paragraphs which areclassified 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. In this article, a paragraph is encoded into a weighted and undirected graph and it is represented into a list of edges.

Comments: 13 Pages.

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Submission history

[v1] 2024-06-03 21:03:31

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