论文标题
掩盖摘要以产生事实不一致的摘要，以改善事实一致性检查
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
论文作者
论文摘要
尽管抽象性摘要系统最近取得了进步，但仍然很难确定生成的摘要是否与源文本一致。为此，最新的方法是在事实一致且不一致的摘要上培训事实一致性分类器。幸运的是，前者在现有摘要数据集中很容易作为参考摘要。但是，产生后者仍然是一个挑战，因为它们实际上需要不一致，但与源文本非常相关才能有效。在本文中，我们建议使用源文本和用密钥信息掩盖的参考摘要生成实际不一致的摘要。七个基准数据集的实验表明，使用我们的方法生成的摘要培训的事实一致性分类器通常超过现有模型，并表现出与人类判断的竞争相关性。我们还分析了使用我们的方法生成的摘要的特征。我们将在https://github.com/hwanheelee1993/mfma上发布预训练的模型和代码。
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.
