Enabling text readability awareness during the micro planning phase of NLG applications

Currently, there is a lack of text complexity awareness in NLG systems. Much attention has been given to text simplification. However, based upon results of an experiment, we unveiled that sophisticated readers in fact would rather read more sophisticated text, instead of the simplest text they could get. Therefore, we propose a technique that considers different readability levels during the micro planning phase of an NLG system. Our technique considers grammatical and syntactic choices, as well as lexical items, when generating text. The application uses the domain of descriptive summaries of line graphs as its use case. The technique proposed uses learning for identifying features of text complexity; a graph search algorithm for efficient aggregation given a target reading level, and a combination of language modeling and word vectors for the creation of a domain-aware synset which allows the creation of disambiguated lexicon that is appropriate to different reading levels. We found that generating text at different target reading levels is indeed preferred by readers with varying reading abilities. To the best of our knowledge, this is the first time readability awareness is considered in the micro planning phase of NLG systems.