  Fact verification models have enjoyed a fast advancement in the last two
years with the development of pre-trained language models like BERT and the
release of large scale datasets such as FEVER. However, the challenging problem
of fake news detection has not benefited from the improvement of fact
verification models, which is closely related to fake news detection. In this
paper, we propose a simple yet effective approach to connect the dots between
fact verification and fake news detection. Our approach first employs a text
summarization model pre-trained on news corpora to summarize the long news
article into a short claim. Then we use a fact verification model pre-trained
on the FEVER dataset to detect whether the input news article is real or fake.
Our approach makes use of the recent success of fact verification models and
enables zero-shot fake news detection, alleviating the need of large-scale
training data to train fake news detection models. Experimental results on
FakenewsNet, a benchmark dataset for fake news detection, demonstrate the
effectiveness of our proposed approach.
