  Currently used metrics for assessing summarization algorithms do not account
for whether summaries are factually consistent with source documents. We
propose a weakly-supervised, model-based approach for verifying factual
consistency and identifying conflicts between source documents and a generated
summary. Training data is generated by applying a series of rule-based
transformations to the sentences of source documents. The factual consistency
model is then trained jointly for three tasks: 1) identify whether sentences
remain factually consistent after transformation, 2) extract a span in the
source documents to support the consistency prediction, 3) extract a span in
the summary sentence that is inconsistent if one exists. Transferring this
model to summaries generated by several state-of-the art models reveals that
this highly scalable approach substantially outperforms previous models,
including those trained with strong supervision using standard datasets for
natural language inference and fact checking. Additionally, human evaluation
shows that the auxiliary span extraction tasks provide useful assistance in the
process of verifying factual consistency.
