A comparative analysis on Hindi and English extractive text summarization

Abstract

Text summarization is the process of transfiguring a large documental information into a clear and concise form. In this article, we present a detailed comparative study of various extractive methods for automatic text summarization on Hindi and English text datasets of news articles. We consider 13 different summarization techniques, namely, TextRank, LexRank, Luhn, LSA, Edmundson, ChunkRank, TGraph, UniRank, NN-ED, NN-SE, FE-SE, SummaRuNNer, and MMR-SE, and we evaluate their performance using various performance metrics, such as precision, recall, F1, cohesion, non-redundancy, readability, and significance. A thorough analysis is done in eight different parts that exhibits the strengths and limitations of these methods, effect of performance over the summary length, impact of language of a document, and other factors as well. A standard summary evaluation tool (ROUGE) and extensive programmatic evaluation using Python 3.5 in Anaconda environment are used to evaluate their outcome. © 2019 Association for Computing Machinery.