This Repo contains a notebook that is used to finetune the popular text summarization model named T5 and BART on CNN Daily Mail dataset.
T5 (Text-To-Text Transfer Transformer) is a popular text summarization model developed by Google. It was introduced in the paper titled "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" by Colin Raffel et al. in 2019. T5 is built on the Transformer architecture, which is known for its effectiveness in sequence-to-sequence tasks.
BART (Bidirectional and Auto-Regressive Transformers) is a text generation model introduced by Facebook AI Research. It was presented in the paper titled "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" by Mike Lewis et al. in 2019. BART is built upon the Transformer architecture and is particularly effective in various text generation tasks, including text summarization.
| Experiment | Training dataset | Epochs | Rouge-1 | Rouge-2 | Rouge-l | Training Time |
|---|---|---|---|---|---|---|
| Original paper | Full dataset | - | 43.52 | 21.55 | 40.69 | - |
| T5 | 1000 | 10 | 33 | 13.9 | 31.4 | 30 mins |
| T5 | 50000 | 3 | 31.31 | 13.1 | 29.69 | 12 hrs 20 min |
| T5 | 10000 | 3 | 32.9 | 14.9 | 31.5 | 2 hrs |
Evaluation Metric Values for Different Summarization Methods on CNN/Daily Mail dataset(after fine tuning BART Model)
The trained model were large enough to store on github, so we have stored them on google drive which can be accessible on Trained models
Open colab and do run all, the notebooks will run automatically producing all the results, no need of any furtherchanges.