  Summarisation of research results in plain language is crucial for promoting
public understanding of research findings. The use of Natural Language
Processing to generate lay summaries has the potential to relieve researchers'
workload and bridge the gap between science and society. The aim of this
narrative literature review is to describe and compare the different text
summarisation approaches used to generate lay summaries. We searched the
databases Web of Science, Google Scholar, IEEE Xplore, Association for
Computing Machinery Digital Library and arXiv for articles published until 6
May 2022. We included original studies on automatic text summarisation methods
to generate lay summaries. We screened 82 articles and included eight relevant
papers published between 2020 and 2021, all using the same dataset. The results
show that transformer-based methods such as Bidirectional Encoder
Representations from Transformers (BERT) and Pre-training with Extracted
Gap-sentences for Abstractive Summarization (PEGASUS) dominate the landscape of
lay text summarisation, with all but one study using these methods. A
combination of extractive and abstractive summarisation methods in a hybrid
approach was found to be most effective. Furthermore, pre-processing approaches
to input text (e.g. applying extractive summarisation) or determining which
sections of a text to include, appear critical. Evaluation metrics such as
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) were used, which do
not consider readability. To conclude, automatic lay text summarisation is
under-explored. Future research should consider long document lay text
summarisation, including clinical trial reports, and the development of
evaluation metrics that consider readability of the lay summary.
