N ap SS : Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023)

Abstract: 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 .

## introduction the medical literature is vast, and continues to expand quickly. most patients (laypeople), however, are unable to access this information because it is written for specialists and so dense and laden with jargon. as the recent 'infodemic' has shown, access to reliable and comprehensible information about citizens' health is a fundamental need: for example, a european health literacy survey (hls-eu) reports that "at least 1 in 10 (12%) respondents s1. two good quality randomized controlled trials involving 754 women were identified. s2. rapid negative pressure application reduced the duration of the procedure without any evidence of differences in outcomes for the mother or infant. s3. rapid method of negative pressure application should be recommended for vacuum extraction assisted vaginal delivery. ## methods we first define the paragraph-level text simplification task, introducing the relevant notations, and then present the napss model. ## conclusions we proposed a summarize-then-simplify twostage model-napss-for paragraph-level medical text simplification. the first component is a "simplification-oriented" summarizer, which we trained over a heuristically derived set of "psuedo" references derived via sentence matching. at inference time, the summarizer extracts the most relevant content to be simplified. this is combined with an additional "narrative prompt" intended to promote consistency, and then passed to an encoderdecoder model to produce the simplified text. experiments on a paragraph-level medical text simplification showed that, under several automatic metrics and human evaluation (involving "laypeople" and medical specialists), this method realized significant improvements with respect to both simplification quality and consistency.