  Text summarization can be classified into two approaches: extraction and
abstraction. This paper focuses on extraction approach. The goal of text
summarization based on extraction approach is sentence selection. One of the
methods to obtain the suitable sentences is to assign some numerical measure of
a sentence for the summary called sentence weighting and then select the best
ones. The first step in summarization by extraction is the identification of
important features. In our experiment, we used 125 test documents in DUC2002
data set. Each document is prepared by preprocessing process: sentence
segmentation, tokenization, removing stop word, and word stemming. Then, we use
8 important features and calculate their score for each sentence. We propose
text summarization based on fuzzy logic to improve the quality of the summary
created by the general statistic method. We compare our results with the
baseline summarizer and Microsoft Word 2007 summarizers. The results show that
the best average precision, recall, and f-measure for the summaries were
obtained by fuzzy method.
