  Automatic abstractive summaries are found to often distort or fabricate facts
in the article. This inconsistency between summary and original text has
seriously impacted its applicability. We propose a fact-aware summarization
model FASum to extract and integrate factual relations into the summary
generation process via graph attention. We then design a factual corrector
model FC to automatically correct factual errors from summaries generated by
existing systems. Empirical results show that the fact-aware summarization can
produce abstractive summaries with higher factual consistency compared with
existing systems, and the correction model improves the factual consistency of
given summaries via modifying only a few keywords.
