There is an enormous amount of information available in the form of documents, articles, links, webpages, etc., which can`t be read completely until effectively summarized. Different procedures are effectively used to separate the imperative information from data to produce summary. This paper gives a brief description of text summarization and deep learning approach called Recurrent Neural Networks (RNNs). Recent advances in Deep RNN methods like Sequence to Sequence, Generative Adversarial Networks, etc. show remarkable results for text summarization. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are discussed to overcome the problem of Vanishing or Exploding Gradient.

RNN, Sequence to Sequence, Vanishing gradient, LSTM, GRU, Deep Recurrent Generative Decoder, GANs

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