Paper Abstract Writing through Editing Mechanism
This work addresses the challenge of automated scientific writing for researchers and non-experts, though it is incremental as it builds on existing neural sequence-to-sequence models.
The paper tackles the problem of automatically generating paper abstracts from titles using a novel Writing-editing Network, achieving Turing test pass rates of up to 30% with junior domain experts and 80% with non-experts.
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%.