  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 ER segmentation and remaining deficiencies
compared to expert-driven human segmentation.
