  Recently, Logic Explained Networks (LENs) have been proposed as
explainable-by-design neural models providing logic explanations for their
predictions. However, these models have only been applied to vision and tabular
data, and they mostly favour the generation of global explanations, while local
ones tend to be noisy and verbose. For these reasons, we propose LENp,
improving local explanations by perturbing input words, and we test it on text
classification. Our results show that (i) LENp provides better local
explanations than LIME in terms of sensitivity and faithfulness, and (ii) logic
explanations are more useful and user-friendly than feature scoring provided by
LIME as attested by a human survey.
