  We present a paper abstract writing system based on an attentive neural
sequence-to-sequence model that can take a title as input and automatically
generate an abstract. We design a novel Writing-editing Network that can attend
to both the title and the previously generated abstract drafts and then
iteratively revise and polish the abstract. With two series of Turing tests,
where the human judges are asked to distinguish the system-generated abstracts
from human-written ones, our system passes Turing tests by junior domain
experts at a rate up to 30% and by non-expert at a rate up to 80%.
