With an ever-increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language processing. There is an increasing demand for automatic methods for text summarization and it is of great importance to solve information overload. To solve the problem a neural network has been created which generates extractive text summary. This paper describes the mechanism implemented to generate Extractive Text Summary from Single Document using Multi-Layer Perceptron. Summarization has been carried out by extracting features from each sentence, training the neural network with error back propagation technique with single hidden layer. The trained model was then tested with manually generated summary and accuracy of 85% was obtained. Finally, the trained model has been implemented to generate Extractive Summary.