Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates.  The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.  Our abstractor is practically identical to the one proposed in BIBREF8. 
 Question: What's the method used here?
Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.