  Accessing medical literature is difficult for laypeople as the content is
written for specialists and contains medical jargon. Automated text
simplification methods offer a potential means to address this issue. In this
work, we propose a summarize-then-simplify two-stage strategy, which we call
NapSS, identifying the relevant content to simplify while ensuring that the
original narrative flow is preserved. In this approach, we first generate
reference summaries via sentence matching between the original and the
simplified abstracts. These summaries are then used to train an extractive
summarizer, learning the most relevant content to be simplified. Then, to
ensure the narrative consistency of the simplified text, we synthesize
auxiliary narrative prompts combining key phrases derived from the syntactical
analyses of the original text. Our model achieves results significantly better
than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute
improvements in terms of lexical similarity, and providing a further 1.1%
improvement of SARI score when combined with the baseline. We also highlight
shortcomings of existing evaluation methods, and introduce new metrics that
take into account both lexical and high-level semantic similarity. A human
evaluation conducted on a random sample of the test set further establishes the
effectiveness of the proposed approach. Codes and models are released here:
https://github.com/LuJunru/NapSS.
