Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique. (15th November 2019)

• Record Type: Journal Article
• Authors: Patel, Darshna; Shah, Saurabh; Chhinkaniwala, Hitesh
• Is Part Of: Expert systems with applications. Volume 134(2019)
• Page Start: 167
• Page End: 177
• Publication Date: 2019-11-15
• Subjects: Feature extraction -- Fuzzy logic -- Text summarization -- Sentence scoring -- Cosine similarity -- Rouge -- Machine learning -- Extrinsic evaluation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.05.045
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Statistical feature based extractive approach for multi-document summarization. Fuzzy logic system is used for sentence scoring. Cosine similarity measure is used to tackle redundancy from summary. Abstract: Nowadays abundant amount of information is available on Internet which makes it difficult for the users to locate desired information. Automatic methods are needed to efficiently sieve and scavenge useful information from the Internet. Text summarization is identified and accepted as one of the solutions to find desired contents from one or more documents. The objective of proposed multi-document summarization is to gain good content coverage with information diversity. The proposed statistical feature based model utilizes the fuzzy model to deal with the imprecise and uncertainty of feature weight. Redundancy removal using cosine similarity is presented as enrichment to proposed work. The proposed approach is compared with DUC (Document Understanding Conference) participant systems and other summarization systems such as TexLexAn, ItemSum, Yago Summarizer, MSSF and PatSum using ROUGE measure on dataset DUC 2004. The experimental results show that our proposed work achieves a significant performance improvement over the other summarizers.