  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 effective. In this paper, we propose to generate factually inconsistent
summaries using source texts and reference summaries with key information
masked. Experiments on seven benchmark datasets demonstrate that factual
consistency classifiers trained on summaries generated using our method
generally outperform existing models and show a competitive correlation with
human judgments. We also analyze the characteristics of the summaries generated
using our method. We will release the pre-trained model and the code at
https://github.com/hwanheelee1993/MFMA.
