  Despite recent advances in abstractive summarization, current summarization
systems still suffer from content hallucinations where models generate text
that is either irrelevant or contradictory to the source document. However,
prior work has been predicated on the assumption that any generated facts not
appearing explicitly in the source are undesired hallucinations. Methods have
been proposed to address this scenario by ultimately improving `faithfulness'
to the source document, but in reality, there is a large portion of entities in
the gold reference targets that are not directly in the source. In this work,
we show that these entities are not aberrations, but they instead require
utilizing external world knowledge to infer reasoning paths from entities in
the source. We show that by utilizing an external knowledge base, we can
improve the faithfulness of summaries without simply making them more
extractive, and additionally, we show that external knowledge bases linked from
the source can benefit the factuality of generated summaries.
