Automatic text summarization (ATS) is becoming increasingly important due to the ever-increasing amount of textual content available online. ATS techniques can be broadly categorized as extractive or abstractive. While many studies have been conducted on ATS data sets, methods, and techniques, there is still a lack of methods that can generate comprehensive summaries of text. This paper aims to fill this gap by developing a text summarization method based on fuzzy logic using fuzzy rules in fuzzy inference systems. Additionally, the proposed method employs the TF-IDF text analysis approach with fuzzy logic, where fuzzy rules are used to select the most important sentences for the summary. The method is evaluated using an experimental analysis of a curated text dataset. The results show that the proposed method generates complete and effective text summaries, outperforming similar methods.