Network Summarization Through the Lens of Latent Semantic Analysis and Fuzzy Techniques

Extracting text summarization involves the retention of only the most important sentences in a document. Finding the appropriate ranking order of the phrases in the document according to their relevance is a vital step in extractive text summarization. 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. 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. Both of these approaches are objectively compared in order to determine their applicability, particularly when dealing with non-English text in general or Marathi in particular.