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Text Summarization With Seq2Seq Models

Summarize long texts using seq2seq models with Keras

The Gradient Team


Seq2seq models are advantageous for their ability to process text inputs without a constrained length. This tutorial covers encoder-decoder sequence-to-sequence models (seq2seq) in-depth and implements a seq2seq model for text summarization using Keras.

For a more detailed breakdown of the code, check out the following two articles on the Paperspace blog: