  Automated scientific fact checking is difficult due to the complexity of
scientific language and a lack of significant amounts of training data, as
annotation requires domain expertise. To address this challenge, we propose
scientific claim generation, the task of generating one or more atomic and
verifiable claims from scientific sentences, and demonstrate its usefulness in
zero-shot fact checking for biomedical claims. We propose CLAIMGEN-BART, a new
supervised method for generating claims supported by the literature, as well as
KBIN, a novel method for generating claim negations. Additionally, we adapt an
existing unsupervised entity-centric method of claim generation to biomedical
claims, which we call CLAIMGEN-ENTITY. Experiments on zero-shot fact checking
demonstrate that both CLAIMGEN-ENTITY and CLAIMGEN-BART, coupled with KBIN,
achieve up to 90% performance of fully supervised models trained on manually
annotated claims and evidence. A rigorous evaluation study demonstrates
significant improvement in generated claim and negation quality over existing
baselines
