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

arXiv: https://arxiv.org/abs/2302.05574 · HTML: https://mlsanity.com/paper/2302.05574/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [Paragraph-level Simplification of Medical Texts](https://mlsanity.com/paper/2104.05767/) (2021)
- [PHEE: A Dataset for Pharmacovigilance Event Extraction from Text](https://mlsanity.com/paper/2210.12560/) (2022)
- [Evaluating Factuality in Text Simplification](https://mlsanity.com/paper/2204.07562/) (2022)
- [Document-Level Text Simplification: Dataset, Criteria and Baseline](https://mlsanity.com/paper/2110.05071/) (2021)
- [EntSUM: A Data Set for Entity-Centric Summarization](https://mlsanity.com/paper/2204.02213/) (2022)
- [Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies](https://mlsanity.com/paper/2102.08366/) (2021)
- [Open Aspect Target Sentiment Classification with Natural Language Prompts](https://mlsanity.com/paper/2109.03685/) (2021)
- [A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews](https://mlsanity.com/paper/2010.11384/) (2020)
- [CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering](https://mlsanity.com/paper/2011.00519/) (2020)
- [Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation](https://mlsanity.com/paper/2210.12902/) (2022)
- [Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media](https://mlsanity.com/paper/2205.03296/) (2022)

## Built on by (most cited first, 13 of 13)

- [Prompt Engineering for Healthcare: Methodologies and Applications](https://mlsanity.com/paper/2304.14670/) (2023)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [APPLS: Evaluating Evaluation Metrics for Plain Language Summarization](https://mlsanity.com/paper/2305.14341/) (2023)
- [Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study](https://mlsanity.com/paper/2402.15663/) (2024)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology](https://mlsanity.com/paper/2312.09792/) (2023)
- [Cascading Large Language Models for Salient Event Graph Generation](https://mlsanity.com/paper/2406.18449/) (2024)
- [SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist Inference Score](https://mlsanity.com/paper/2410.09632/) (2024)
- [On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization](https://mlsanity.com/paper/2403.05788/) (2024)
- [Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification](https://mlsanity.com/paper/2406.18859/) (2024)
- [Society of Medical Simplifiers](https://mlsanity.com/paper/2410.09631/) (2024)
- [Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study](https://mlsanity.com/paper/2502.16022/) (2025)
- [Helping Figures Tell their Story! Paper-Grounded Video Generation Explaining Complex Scientific Figures](https://mlsanity.com/paper/2606.12576/) (2026)

## Abstract (arXiv)

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.