Encoder-Decoder Models for Text Summarization in Keras

Last Updated on August 7, 2019

Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document.

The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization.

It can be difficult to apply this architecture in the Keras deep learning library, given some of the flexibility sacrificed to make the library clean, simple, and easy to use.

In this tutorial, you will discover how to implement the Encoder-Decoder architecture for text summarization in Keras.

After completing this tutorial, you will know:

• How text summarization can be addressed using the Encoder-Decoder recurrent neural network architecture.
• How different encoders and decoders can be implemented for the problem.
• Three models that you can use to implemented the architecture for text summarization in Keras.

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