BioNLP Workshop

BioNLP 2023 and Shared Tasks @ ACL 2023: https://aclweb.org/aclwiki/BioNLP_Workshop.

Paper: VBD-NLP at BioLaySumm Task 1: Explicit and Implicit Key Information Selection for Lay Summarization on Biomedical Long Documents

We achieved the Third Rank in the shared task (and the Second Rank excluding the baseline submission of the organizers).

The performance on the private-test set of our best system and compared with the top-ranked systems: Top-1 (MDC), Top-2 (Baselines) reported in the shared task leaderboard.

	R-1 ↑	R-2 ↑	R-L ↑	BERTScore ↑	FKGL ↓	DCRS ↓	BARTScore ↑
Top-1 (MDC)	0.4822	0.1553	0.4485	0.8707	12.9370	10.2058	-1.1771
Top-2 (Baseline)	0.4696	0.1445	0.4371	0.8642	12.0694	10.2487	-0.8305
Our Approach	0.4829	0.1469	0.4502	0.8571	12.2923	10.0862	-1.7357
Our Approach (PLOS)	0.4853	0.1711	0.4473	0.8617	14.8063	11.5870	-1.3791
Our Approach (eLife)	0.4805	0.1227	0.4532	0.8526	9.7781	8.5854	-2.0924

Dataset & pre-trained models

• Processed data & pre-trained models: https://drive.google.com/file/d/1MhI4RRKCnwPC2txCSSaYCsaLk28uIuWO/view?usp=sharing

• Raw Dataset:

  • Train: https://drive.google.com/file/d/1FFfa4fHlhEAyJZIM2Ue-AR6Noe9gOJOF/view?usp=share_link
  • Private-Test: https://drive.google.com/file/d/1qHogbSW99UfhdbpFUhP6buDix5-8B2HC/view?usp=sharing

How to use

• STEP 1:

Make sure the data for all splits are available (processing of the training sets might take several minutes):

>>> python -m factorsum.data prepare_dataset data/PLOS plos

>>> python -m factorsum.data prepare_dataset data/eLife elife

• STEP 2:

Run the training script as follows:

>>> bash plos_run_train.sh

>>> bash elife_run_train.sh

• STEP 3:

Inference as follows:

>>> plos_infer.sh

>>> elife_infer.sh