NotXia / biomed-ext-summ

Biomedical extractive summarization

Biomedical extractive summarization

Work done for my Bachelor's thesis.

An evaluation of general-domain and biomedical pretrained language models for biomedical extractive summarization.

Available weights

Fine tuned weights for the following models are available on Hugging Face:

Model	Hugging Face
Longformer	NotXia/longformer-bio-ext-summ
PubMedBERT	NotXia/pubmedbert-bio-ext-summ

Installation

Move into src and run:

pip install -r requirements.txt
python -m spacy download en_core_web_sm

Preprocessing

A dataset has to be first converted into an extractive form.
Available datasets are: cnn_dailymail, ms2, cochrane and sumpubmed.

python abs2ext.py                               \
        --dataset=<dataset name>                \
        --output=<path to output directory>     \
        --proc=<number of processes to create>

Then, an extractive dataset has to be tokenized for a specific model.
Supported model families are: BERT, RoBERTa, DistilBERT, MobileBERT and Longformer.

python preprocess.py                            \
    --dataset-dir=<path to extractive dataset>  \
    --model=<HuggingFace model to tokenize for> \
    --output=<path to output directory>         \
    --proc=<number of processes to create>

Training

To train a model run:

python train.py                                             \
    --model=<HuggingFace model>                             \
    --dataset=<path to tokenized dataset>                   \
    --epochs=<training epochs>                              \
    --lr=<learning rate>                                    \
    --batch-size=<batch size>                               \
    --accum-steps=<gradient accumulation steps>             \
    --checkpoint-best                                       \
    --checkpoints-freq=<number of epochs>                   \
    --checkpoints-path=<path where checkpoints are saved>   \
    --history-path=<path where training history is saved>

If mixed precision is needed, add --mixed-precision.

Evaluating

To evaluate a trained model, run:

python eval.py                                      \
    --checkpoint=<path to model checkpoint>         \
    --dataset=<path to extractive dataset>          \
    --splits=<comma separated splits>               \
    --strategy-count=<number of selected sentences>

Other summarization strategy can be used instead of strategy-count.
Run python eval.py -h for more details.

To evaluate using the Oracle, run:

python eval.py                              \
    --checkpoint=<path to model checkpoint> \
    --dataset=<path to extractive dataset>  \
    --splits=<comma separated splits>       \
    --oracle

To evaluate using LEAD-N, run:

python eval.py                              \
    --checkpoint=<path to model checkpoint> \
    --dataset=<path to extractive dataset>  \
    --splits=<comma separated splits>       \
    --lead=<number of sentences>