EASumm

Overview

Code and data accompanying the paper "Graph-Enhanced Biomedical Abstractive Summarization via Factual Evidence Extraction", extended by "Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers" (Best Studen Paper Award @ DATA22).

EASumm is the first abstractive summarization model augmenting source documents with explicit, structured medical evidence extracted from them, thereby concretizing a tandem text-graph architecture.

Install requirements

pip install -r requirements.txt
pip install torch==1.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip install torch-geometric

Download events extracted with DeepEventMine

cd deep_event_mine
gdown 1x3oHfAKdtYfTEKuLPFTV_b2foA-VEMSx

Train our model

python train_abstractor.py --wandb_log

Decode

python decode_abstractor.py  --model_dir ckpts

Evaluate

Download ROUGE-1.5.5 and tell pyrouge the ROUGE path

gdown 1Df0FY4k-EGbvOlIBk2-Ih7J5N5ss-Ko4
tar -xvf ROUGE.tar.gz
rm ROUGE.tar.gz
pyrouge_set_rouge_path $(pwd)/ROUGE

python eval_full_model.py  --decode_dir ckpts 

✉ Contacts

• Giacomo Frisoni, giacomo.frisoni[at]unibo.it
• Paolo Italiani, paolo.italiani[at]studio.unibo.it
• Gianluca Moro, gianluca.moro[at]unibo.it

If you have troubles, suggestions, or ideas, the Discussion board might have some relevant information. If not, you can post your questions there 💬🗨.

License

This project is released under the CC-BY-NC-SA 4.0 license (see LICENSE).

Cite

If you use EASumm in your research, please cite:

  @inproceedings{DBLP:conf/data/FrisoniIBM22,
    author    = {Giacomo Frisoni and
                Paolo Italiani and
                Francesco Boschi and
                Gianluca Moro},
    editor    = {Alfredo Cuzzocrea and
                Oleg Gusikhin and
                Wil M. P. van der Aalst and
                Slimane Hammoudi},
    title     = {Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers},
    booktitle = {Proceedings of the 11th International Conference on Data Science,
                Technology and Applications, {DATA} 2022, Lisbon, Portugal, July 11-13,
                2022},
    pages     = {168--179},
    publisher = {{SCITEPRESS}},
    year      = {2022},
    url       = {https://doi.org/10.5220/0011354900003269},
    doi       = {10.5220/0011354900003269},
    timestamp = {Wed, 03 Aug 2022 15:53:22 +0200},
    biburl    = {https://dblp.org/rec/conf/data/FrisoniIBM22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }