text: The authors report a number of experiments using off-the-shelf sentence embedding methods for performing extractive summarisation, using a number of simple methods for choosing the extracted sentences.  Unfortunately the contribution is too minor, and the work too incremental, to be worthy of a place at a top-tier international conference such as ICLR.  The overall presentation is also below the required standard.  The work would be better suited for a focused summarisation workshop,  where there would be more interest from the participants. Some of the statements motivating the work are questionable.  I don't know if sentence vectors *in particular* have been especially successful in recent NLP (unless we count neural MT with attention as using "sentence vectors").  It's also not the case that the sentence reordering and text simplification problems have been solved, as is suggested on p.2.  The most effective method is a simple greedy technique.  I'm not sure I'd describe this as being "based on fundamental principles of vector semantics" (p.4). The citations often have the authors mentioned twice. The reference to "making or breaking applications" in the conclusion strikes me as premature to say the least.