  While recent work on automated fact-checking has focused mainly on verifying
and explaining claims, for which the list of claims is readily available,
identifying check-worthy claim sentences from a text remains challenging.
Current claim identification models rely on manual annotations for each
sentence in the text, which is an expensive task and challenging to conduct on
a frequent basis across multiple domains. This paper explores methodology to
identify check-worthy claim sentences from fake news articles, irrespective of
domain, without explicit sentence-level annotations. We leverage two internal
supervisory signals - headline and the abstractive summary - to rank the
sentences based on semantic similarity. We hypothesize that this ranking
directly correlates to the check-worthiness of the sentences. To assess the
effectiveness of this hypothesis, we build pipelines that leverage the ranking
of sentences based on either the headline or the abstractive summary. The
top-ranked sentences are used for the downstream fact-checking tasks of
evidence retrieval and the article's veracity prediction by the pipeline. Our
findings suggest that the top 3 ranked sentences contain enough information for
evidence-based fact-checking of a fake news article. We also show that while
the headline has more gisting similarity with how a fact-checking website
writes a claim, the summary-based pipeline is the most promising for an
end-to-end fact-checking system.
