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Hands-On Natural Language Processing with Python by Rajalingappaa Shanmugamani, Rajesh Arumugam

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State-of-the-art abstractive text summarization

In this section, we will look at two recent papers that describe enhancements to the model used in our news text summarization example from the previous section.

In the first paper, Abstractive Text Summarization Using Sequence-To-Sequence RNNs and Beyond (https://arxiv.org/abs/1602.06023), from IBM, Ramesh Nallapati, et al., applied the model for neural machine translation to text summarization and achieved better performance, as compared to state-of-the-art systems. This model uses a bidirectional GRU-RNN as an encoder, and a unidirectional GRU-RNN as a decoder. Note that this is the same model architecture that we used in our news summarization example.

The following are the main additional ...

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