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The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Additionally, we find an effective way to improve current frameworks and achieve the state-ofthe-artdoi:10.18653/v1/p19-1100 dblp:conf/acl/ZhongLWQH19 fatcat:rldnxmzjfrhs3bjjer7vpqvd5y