Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms

Abstract

Extractive text summarization involves the retention of only the most important sentences in a document. In the past, multiple approaches involving both statistical and machine learning-based methods have been used for this task. The crucial step in extractive text summarization is getting the right ranking order of sentences in the document in terms of their importance. Singular value decomposition or SVD algorithm based on latent semantic analysis focuses on recognizing the sections in the document which are related in terms of their semantic nature. Fuzzy algorithms involve reasoning of the priority order of the sentences using fuzzy logic unlike the use of discrete values. While significant work has been done for extractive text summarization in English and other foreign languages, there is ample scope for improving the performance of systems when dealing with Marathi text. In this paper, SVD and fuzzy algorithms are proposed for performing extractive text summarization on Marathi documents. Work is done upon the modeling principle, data flow, and parameters of these algorithms such that they are best suited for the task. An analysis of the characteristics of both these techniques is conducted to compare their benefits and shortcomings. The performance of both the algorithms is evaluated on a document dataset using standard performance metrics including the ROUGE metric. An unbiased comparison of both these techniques is carried out to inform the applicability of them, especially when working with Marathi or in general, non-English text.