Model based on BERT, employed in a regression task to predict the Rouge-2 of a sentence with respect to the highlights of the paper. Starting from the model proposed with the paper morenolq/thext-bio-scibert we performed an additional fine-tuning contextualizing the sentence with our custom context, namely PCE-best. The additional training epoch was performed on BIOPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").
You can find more details in the GitHub repo.
Usage
Tis checkpoint should be loaded into BertForSequenceClassification.from_pretrained. See the BERT docs for more information.
Metrics
We tested the model on BIOPubSumm with the following results:
| BIOPubSumm | |
|---|---|
| Rouge-1 F1 | 0.3335 |
| Rouge-2 F1 | 0.1222 |
| Rouge-L F1 | 0.3038 |
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