Being able to read and understand written text is critical in a digital era.
However, studies shows that a large fraction of the population experiences
comprehension issues. In this context, further initiatives in accessibility are
required to improve the audience text comprehension. However, writers are
hardly assisted nor encouraged to produce easy-to-understand content. Moreover,
Automatic Text Simplification (ATS) model development suffers from the lack of
metric to accurately estimate comprehension difficulty We present
\textsc{LC-Score}, a simple approach for training text comprehension metric for
any French text without reference \ie predicting how easy to understand a given
text is on a $[0, 100]$ scale. Our objective with this scale is to
quantitatively capture the extend to which a text suits to the \textit{Langage
Clair} (LC, \textit{Clear Language}) guidelines, a French initiative closely
related to English Plain Language. We explore two approaches: (i) using
linguistically motivated indicators used to train statistical models, and (ii)
neural learning directly from text leveraging pre-trained language models. We
introduce a simple proxy task for comprehension difficulty training as a
classification task. To evaluate our models, we run two distinct human
annotation experiments, and find that both approaches (indicator based and
neural) outperforms commonly used readability and comprehension metrics such as
FKGL and SAMSA.

我们提出了一个简单的方法 LC-Score，用于训练法语文本的阅读理解指标，通过预测给定文本的易读性来定量捕捉文本与清晰语言指南的契合程度。我们探索了两种方法：使用语言学动机指标训练统计模型，以及从文本中直接利用预训练语言模型进行神经学习。我们通过两个人工注释实验评估了我们的模型，发现两种方法（基于指标和神经）优于常用的可读性和理解度量，如 FKGL 和 SAMSA。