  Text Summarization has been an extensively studied problem. Traditional
approaches to text summarization rely heavily on feature engineering. In
contrast to this, we propose a fully data-driven approach using feedforward
neural networks for single document summarization. We train and evaluate the
model on standard DUC 2002 dataset which shows results comparable to the state
of the art models. The proposed model is scalable and is able to produce the
summary of arbitrarily sized documents by breaking the original document into
fixed sized parts and then feeding it recursively to the network.
