Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking. (arXiv:2205.02035v1 [cs.CL])

Web: http://arxiv.org/abs/2205.02035

Despite the recent advances in abstractive summarization systems, it is still
difficult to determine whether a generated summary is factual consistent with
the source text. To this end, the latest approach is to train a factual
consistency classifier on factually consistent and inconsistent summaries.
Luckily, the former is readily available as reference summaries in existing
summarization datasets. However, generating the latter remains a challenge, as
they need to be factually inconsistent, yet closely relevant to the source text
to be …

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