  Different from general documents, it is recognised that the ease with which
people can understand a biomedical text is eminently varied, owing to the
highly technical nature of biomedical documents and the variance of readers'
domain knowledge. However, existing biomedical document summarization systems
have paid little attention to readability control, leaving users with summaries
that are incompatible with their levels of expertise. In recognition of this
urgent demand, we introduce a new task of readability controllable
summarization for biomedical documents, which aims to recognise users'
readability demands and generate summaries that better suit their needs:
technical summaries for experts and plain language summaries (PLS) for laymen.
To establish this task, we construct a corpus consisting of biomedical papers
with technical summaries and PLSs written by the authors, and benchmark
multiple advanced controllable abstractive and extractive summarization models
based on pre-trained language models (PLMs) with prevalent controlling and
generation techniques. Moreover, we propose a novel masked language model (MLM)
based metric and its variant to effectively evaluate the readability
discrepancy between lay and technical summaries. Experimental results from
automated and human evaluations show that though current control techniques
allow for a certain degree of readability adjustment during generation, the
performance of existing controllable summarization methods is far from
desirable in this task.
