Automating Easy Read Text Segmentation
This addresses the need for accessible information creation for people with reading difficulties, but it is incremental as it builds on existing language models and parsing techniques.
The paper tackled the problem of automatically segmenting Easy Read text to aid people with reading difficulties, showing that automated methods are viable but still have deficiencies compared to human experts.
Easy Read text is one of the main forms of access to information for people with reading difficulties. One of the key characteristics of this type of text is the requirement to split sentences into smaller grammatical segments, to facilitate reading. Automated segmentation methods could foster the creation of Easy Read content, but their viability has yet to be addressed. In this work, we study novel methods for the task, leveraging masked and generative language models, along with constituent parsing. We conduct comprehensive automatic and human evaluations in three languages, analysing the strengths and weaknesses of the proposed alternatives, under scarce resource limitations. Our results highlight the viability of automated Easy Read text segmentation and remaining deficiencies compared to expert-driven human segmentation.