Code and data for our EACL 2023 finding paper "NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization" which can be found: https://arxiv.org/abs/2302.05574. We built our codes upon the repository: "Paragraph-level Simplification of Medical Texts".

It takes around 3.5 hours to finetune NapSS from scratch, on 2 Quadro RTX 6000 GPUs.

• We list the packages in our environment in env.yml file for your reference.
• All data we used are provided in data folder.

• (30 mins) Extractive narrative key phrases: python3 scripts/utils/extract_narrative_keys.py data/data_final_1024.json data/data_final_1024-keys.json. You can mute pls key phrases extraction for saving time.
• (20 mins) Attach intermediate summarization labels for stage one training: python3 scripts/utils/attach_summary_labels.py data/data_final_1024-keys.json data/data_final_1024-keyswithlabels.json

• Prepare summarization data folder: mkdir data/data-summarization
• (12 mins) Prepare summarization train, dev and test sets: python3 scripts/utils/create_summarization_data.py data/data_final_1024-keyswithlabels.json
• (24 mins) Finetune classification-based summarization model: python3 scripts/summarization/train.py. You can turn on wandb logging by setting os.environ["WANDB_DISABLED"] = "false" in the script.
• (Optional) Evaluate finetuned summarization model: python3 scripts/summarization/evaluate.py
• Prepare new corpus folder of stage 2: cp -r data/data-1024 data/data-1024-napss
• (4 mins) predict over dev and test sets to create new sets of stage 2: python3 scripts/summarization/predict.py
• (10 mins) for train set, we don't predict but compose from pre-extracted keys and summaries: python3 scripts/utils/create_new_trainset.py data/data_final_1024-keyswithlabels.json

• (20 mins) Train NapSS: bash scripts/train/bart-napss.sh
• (100 mins) Generate simplified text via NapSS: bash scripts/generate/bart-gen-napss.sh
• (Optional) Run bash scripts/train/bart-napss-ul.sh for NapSS + UL joint training. We use "cochrane" settings, you can check detailed instruction for other settings in Paragraph-level Simplification of Medical Texts.
• (Optional) Generate simplified text via joint model: bash scripts/generate/bart-gen-napss-ul.sh
• (Optional) Use above scripts and finetuned models for zero-shot inference on OOD TICO-19 dataset: data/data-tico19

• We provide finetuned models here: https://drive.google.com/drive/folders/1CHBsuI4aEfjM_ds-FzQW_Hp5JeW_ajYy?usp=sharing.

• Calculate evaluation metrics over generated files: python3 scripts/utils/calculate_evaluation_metrics.py trained_models/bart-no-ul-abskeys/gen_nucleus_test_1_0-500.json
• Note: Bertscore computing is provided but muted, since it takes quite long time to compute this metric (e.g, 40 mins).

• We provide two sets (expert and non_experts) human evaluation results in the human evaluation folder.
• The non_experts set include results evaluated by 6 annotators on three asepscts: Simplicity, Fluency and Factuality.
• The expert set include results evaluated by 2 annotators only on Factuality.