Automatic text summarization using feature based fuzzy extraction

Abstract

Automatic text summarization is to compress the original text into a shorter version and help the user to quickly understand large volumes of information. This paper focuses on the automatic text summarization by sentence extraction with important features based on fuzzy logic. In our experiment, we used 6 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, remuving Stop Word and stemming Word. Then, we use 8 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our result with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-mean for the summaries are conducted from fuzzy method.