Benefiting from the success of neural sequence models in other NLP tasks, Cheng and Lapata (2016) propose a novel approach to extractive summarization based on neural networks and continuous sentence features, which outperforms traditional methods on the Dai-lyMail dataset.
they develop a general encoder-decoder architecture, where a CNN is used as sentence encoder, a uni-directional LSTM as document encoder, with another uni-directional LSTM as decoder.