The number of electronic documents as a media of business and academic information has increased tremendously after the introduction of the World Wide Web. Ever since, instances where users being overloaded with too much electronic textual information are inevitable. The users may only be interested in shorter versions of text documents but are overloaded with lengthy texts. The objective of the study is to develop a text summarization system that incorporates learning ability by combining a statistical approach, keywords extraction, and neural network with unsupervised learning. The system is able to learn to classify sentences when well trained with sufficient text samples. Users with strong background in writing English summaries have subjectively evaluated the outputs of the text summarization system based on contents. With the average contents score of 83.03, the system is regarded to have produced an effective summary with most of the important contents of the original text extracted without compromising the summary's readability.