Automated simplification models aim to make input texts more readable. Such
methods have the potential to make complex information accessible to a wider
audience, e.g., providing access to recent medical literature which might
otherwise be impenetrable for a lay reader. However, such models risk
introducing errors into automatically simplified texts, for instance by
inserting statements unsupported by the corresponding original text, or by
omitting key information. Providing more readable but inaccurate versions of
texts may in many cases be worse than providing no such access at all. The
problem of factual accuracy (and the lack thereof) has received heightened
attention in the context of summarization models, but the factuality of
automatically simplified texts has not been investigated. We introduce a
taxonomy of errors that we use to analyze both references drawn from standard
simplification datasets and state-of-the-art model outputs. We find that errors
often appear in both that are not captured by existing evaluation metrics,
motivating a need for research into ensuring the factual accuracy of automated
simplification models.

自动化简模型在提高文本可读性的过程中，如何保证简化之后的文本的准确性是一个需要研究的问题。本文提出了一个误差分类学，用来分析标准简化数据集和最新模型输出的简化文本的准确性问题，发现现有评估度量无法涵盖所有错误，因此需要进一步研究自动化简模型的准确性保障。