The impact of feature selection on text summarisation. (10th April 2015)

• Record Type: Journal Article
• Authors: Jayashree, R.; Murthy, K. Srikanta; Anami, Basavaraj S.; James, Alex Pappachen
• Is Part Of: International journal of applied pattern recognition. Volume 1:Number 4(2014)
• Page Start: 377
• Page End: 400
• Publication Date: 2015-04-10
• Subjects: feature selection -- text summarisation -- text document summaries -- feature extraction -- rank -- score -- term frequency -- Galavotti–Sebastiani–Simi -- GSS -- inverse document frequency -- IDF -- word occurrence count; Pattern perception -- Periodicals; Pattern recognition systems -- Periodicals; 006.405
• Languages: English
• ISSNs: 2049-887X

Abstract

The applicability of using feature selection methods for text document summarisation is relatively an unexplored topic in information retrieval. The ability of feature selection techniques to identify key features within the text document could produce better summaries. In this paper, we put this premise to test, by considering feature selection as an essential preprocessing step for text document summarisation. In this work, we have explored several feature selection methods and their role in text document summarisation. The corpus used is Technology Development for Indian Languages (TDIL) that consists of 483 documents belonging to four categories: aesthetics, commerce, social sciences and natural sciences.