A Supervised Approach for Keyphrase Extraction using SVM

  • Namrata Kumari, Ankit Guleria, Radhika Sood, Pardeep Singh

Abstract

Automatic text summarization is a process of filtering out the most important and relevant part from the original source text documents. Keyphrase extraction helps in eliminating the redundant, less important content and as output it provides the vital information in a shorter size which is usually less than half a length of original text. Support vector machine (SVM) is used for keyphrase extraction in text summarization in this study. The aim of this paper is to establish an understanding of how text can be summarized to extract only the useful, correct and precise information from large data set, thus saving our valuable time.

Keywords: Text Summarization, Extractive Approach, Feature Extraction, Summary Generation and Support Vector Machine.

Published
2019-12-31
How to Cite
Pardeep Singh, N. K. A. G. R. S. (2019). A Supervised Approach for Keyphrase Extraction using SVM. International Journal of Control and Automation, 12(5), 469 - 477. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/2653
Section
Articles