Readability assessment for text simplification

Readability assessment can play a role in the evaluation of a simplification algorithm as well as in the identification of what to simplify. While some previous research used traditional readability formulas to evaluate text simplification, there is little research into the utility of readability assessment for identifying and analyzing sentence level targets for text simplification. We explore this aspect in our paper by first constructing a readability model that is generalizable across corpora and across genres and later adapting this model to make sentence-level readability judgments. First, we report on experiments establishing that the readability model integrating a broad range of linguistic features works well at a document level, performing on par with the best systems on a standard test corpus. Next, the model is confirmed to be transferable to different text genres. Moving from documents to sentences, we investigate the model’s ability to correctly identify the difference in reading level between a sentence and its human simplified version. We conclude that readability models can be useful for identifying simplification targets for human writers and for evaluating machine generated simplifications.