  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.
