The jupyter notebook shows a general way to construct a sequence to sequence model which is not limited to solving text summarization problems. The model has an encoder-decoder structure. The encoder uses a bidirectional recurrent neural network (RNN) consists of Gated Recurrent Units (GRU), and the decoder is another RNN which is used to generate the summary of the input text. An attention mechanism is applied to improve the performance on long inputs.
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This repository uses the sequence-to-sequence model to solve the text summarization problem.
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xiaoouzhang/Text-Summarization
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This repository uses the sequence-to-sequence model to solve the text summarization problem.
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