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}
}