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Accessing medical literature is difficult for laypeople as the content iswritten for specialists and contains medical jargon. Automated textsimplification methods offer a potential means to address this issue. In thiswork, we propose a summarize-then-simplify two-stage strategy, which we callNapSS, identifying the relevant content to simplify while ensuring that theoriginal narrative flow is preserved. In this approach, we first generatereference summaries via sentence matching between the original and thesimplified abstracts. These summaries are then used to train an extractivesummarizer, learning the most relevant content to be simplified. Then, toensure the narrative consistency of the simplified text, we synthesizeauxiliary narrative prompts combining key phrases derived from the syntacticalanalyses of the original text. Our model achieves results significantly betterthan the seq2seq baseline on an English medical corpus, yielding 3%~4% absoluteimprovements in terms of lexical similarity, and providing a further 1.1%improvement of SARI score when combined with the baseline. We also highlightshortcomings of existing evaluation methods, and introduce new metrics thattake into account both lexical and high-level semantic similarity. A humanevaluation conducted on a random sample of the test set further establishes theeffectiveness of the proposed approach. Codes and models are released here:https://github.com/LuJunru/NapSS.

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization