Pegasus XSUM Gigaword
Model description
Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance.
Intended uses & limitations
Produces short summaries with the coherence of the XSUM Model
How to use
# You can include sample code which will be formatted
Limitations and bias
Still has all the biases of any of the abstractive models, but seems a little less prone to hallucination.
Training data
Initialized with pegasus-XSUM
Training procedure
Trained for 11500 iterations on Gigaword corpus using OOB seq2seq (from hugging face using the default parameters)
Eval results
Evaluated on Gigaword test set (from hugging face using the default parameters) run_summarization.py --model_name_or_path pegasus-xsum/checkpoint-11500/ --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate
| Metric | Score |
|---|---|
| eval_rouge1 | 34.1958 |
| eval_rouge2 | 15.4033 |
| eval_rougeL | 31.4488 |
run_summarization.py --model_name_or_path google/pegasus-gigaword --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate
| Metric | Score |
|---|---|
| eval_rouge1 | 20.8111 |
| eval_rouge2 | 8.766 |
| eval_rougeL | 18.4431 |
BibTeX entry and citation info
@inproceedings{...,
year={2020}
}
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