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.
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
cd deep_event_mine
gdown 1x3oHfAKdtYfTEKuLPFTV_b2foA-VEMSx
python train_abstractor.py --wandb_log
python decode_abstractor.py --model_dir ckpts
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
- 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 💬🗨.
This project is released under the CC-BY-NC-SA 4.0 license (see LICENSE).
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}
}
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