With a tremendous amount of data available online, reading an article, running through pages is a tedious process. Giving a sneak peek into the big picture helps to analyze the content and understand the essential message of the document. Text Summarization provides a way to condense the given document in a meaningful way. The summarized content gives a gist of the original document, having the important information to be conveyed to the user. In this paper, automatic text summarization has been performed on single documents using Fuzzy Logic inference rules. The summarization is based on features extracted from the document. For feature extraction, Frequency Based Feature Extraction Technique (FBFET) is applied. This implementation uses NOUN-VERB IDENTIFIER to recognize the prominent category and seek out the features based on their frequency in the entire document. The sentences are scored using the important characteristics for defining the rules for Fuzzy Logic Inference Engine. High priority sentences are extracted to generate the summary of the document. The performance is measured using ROGUE (Recall Oriented Understudy for Gisting Evaluation) method and analyzed. The proposed automatic text summarizer is implemented with the feature extraction module which enhances the process of summarizer.