In the training environment, we design different summarization models to analyze how they influence the performance.
these models differ in the types of architectures (Encoders: CNN, LSTM, Transformer (Vaswani et al., 2017); Decoders: auto-regressive 2 , non auto-regressive), external transferable knowledge (GloVe (Pennington et al., 2014), BERT (Devlin et al., 2018), NEWSROOM (Grusky et al., 2018)) and different learning schemas (supervised learning and reinforcement learning).