High Impact of Rough Set and KMeans Clustering Methods in Extractive Summarization of Journal Articles.

Text Summarization is the technique of shortening a long text but having all the relevant and significant topics conveyed in the text. As the number of journal articles published every year is growing steadily, the relevance of research on journal article summarization also increases. In this work, six extractive summarization methods are implemented and compared with the results of four standard methods applied to the dataset of journal articles. Precision, Recall and F-measure of Rouge-1, Rouge-2, Rouge-L and Rouge-Lsum measures are analyzed. Eight features are used in the implementation of the sum of features method and the BernoulliRBM method. It is observed from the experiments conducted that the Rough set method and the K-Means clustering and summarization method have high rouge scores in 10 out of the 12 measures analyzed here. The recall of the generated summary by the Roughset method is further improved when the first part of the article is used as a heuristic yard in calculating the similarity score with selected sentences. [ABSTRACT FROM AUTHOR]