Deciding on the complexity of a generated text in NLG systems is a contentious task. Some systems propose the generation of simple text for low-skilled readers; some choose what they anticipate to be a "good measure" of complexity by balancing sentence length and number of sentences (using scales such as the D-level sentence complexity) for the text; while others target high-skilled readers. In this work, we discuss an approach that aims to leverage the experience of the reader when reading generated text by matching the syntactic complexity of the generated text to the reading level of the surrounding text. We propose an approach for sentence aggregation and lexical choice that allows generated summaries of line graphs in multimodal articles available online to match the reading level of the text of the article in which the graphs appear. The technique is developed in the context of the SIGHT (Summarizing Information Graphics Textually) system. This paper tackles the micro planning phase of sentence generation discussing additionally the steps of lexical choice, and pronominalization.
NLG systems for the visually impaired have also explored the generation of text for trend data REF around the adaptation of generated descriptions to users' reading levels.
Adapting Graph Summaries to the Users’ Reading Levels
Proceedings of the 8th International Natural Language Generation Conference (INLG)
Proceedings of the 8th International Natural Language Generation Conference (INLG)
Computer Science