Our work is based on the SummaRuNNer model <cite>[5]</cite> . It consists of a two-layer bi-directional Gated Recurrent Unit (GRU) Recurrent Neural Network (RNN) which treats the summarization problem as a binary sequence classification problem, where each sentence is classified sequentially as sentence to be included or not in the summary. However, we introduced two modifications to the original SummaRuNNer architecture, leading to better results while reducing complexity: arXiv:1911.06121v1 [cs.CL] 13 Nov 2019 Fig. 1 .