transformers pytorch bart text2text-generation en autotrain_compatible endpoints_compatible region:us
---
language:
- en
---
BART model used to generate scientific papers' title given the highlights and the abstract of the paper.

This model is the result of a fine-tuning process done on [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
We performed the fine-tuning for one epoch on CSPubSumm (Ed Collins, et al. "A supervised approach to extractive summarisation of scientific papers."), 
BIOPubSumm, and AIPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").

You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).

# Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the 
[BART docs](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) for more information.

# Metrics
We have tested the model on all three the test sets, with the following results:

|   Dataset  | Rouge-1 F1 | Rouge-2 F1 | Rouge-L F1 | BERTScore F1 |
|:----------:|:----------:|:----------:|:----------:|:------------:|
|  AIPubSumm |   0.42713  |   0.21781  |   0.35251  |    0.90391   |
| BIOPubSumm |   0.45758  |   0.25219  |   0.39350  |    0.90205   |
|  CSPubSumm |   0.51502  |   0.33377  |   0.45760  |    0.91703   |