FABRA: French Aggregator-Based Readability Assessment toolkit

In this paper, we present the FABRA readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity and dependency counts while the most important ones for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical gradation in FFL textbooks. FABRA has the potential to support new research on readability assessment for French