  We propose a novel, attention-based self-supervised approach to identify
"claim-worthy" sentences in a fake news article, an important first step in
automated fact-checking. We leverage "aboutness" of headline and content using
attention mechanism for this task. The identified claims can be used for
downstream task of claim verification for which we are releasing a benchmark
dataset of manually selected compelling articles with veracity labels and
associated evidence. This work goes beyond stylistic analysis to identifying
content that influences reader belief. Experiments with three datasets show the
strength of our model. Data and code available at
https://github.com/architapathak/Self-Supervised-ClaimIdentification
