A Comparative Study On Extractive Text Summarization Technique

Text summarization is becoming a significant topic of study now-a-days. Automatic text summarizing is the process of summarizing or producing the large document into concise and shorten one without any human involvement. In this paper we have proposed one approach to summarize the text using fuzzy logic that involves fuzzification, inference engine and defuzzification after feature extraction. A proper comparison of Precision, Recall and F-score of ROUGE 2 and ROUGE L has been done between TexLexAn, ItemSum, Baseline, Yago Summarizer, MSSF, PatSum and proposed model for economic document consisting of 100 lines. Rouge is used to measure the accuracy in test summarization. Dataset is collected from DUC 2004. Out of which proposed model shows the best result. So we have done a brief survey on various approaches for summarizing the document and proposed one approach to include fuzzy logic system after feature extraction to produce the better result on different domains.