Most recent works on neural extractive summarization have been rather successful in generating summaries of short news documents (around 650 words/document) (Nallapati et al., 2016) by applying neural Seq2Seq models (Cheng and Lapata, 2016).
when it comes to long documents, these models tend to struggle with longer sequences because at each decoding step, the decoder needs to learn to construct a context vector capturing relevant information from all the tokens in the source sequence (Shao et al., 2017).