Entity-based-SpanCopy

Official code for Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency

Dependencies

python == 3.7.11

pytorch == 1.10.0

pytorch_lightning == 1.5.3

transformers == 4.11.3

Usage

SpanCopy Model

To train the SpanCopy model with global relevance, run with --use_global_relevance with proper --beta. The beta used in our experiments is set by grid search on small subsets of each dataset (2k for training and 200 for validation).

Dataset	beta
CNNDM	0.5
CNNDM-filtered	0.5
XSum	0.6
XSum-filtered	0.9
Pubmed	0.4
Pubmed-filtered	0.4
arXiv	0.4
arXiv-filtered	0.5

To train the SpanCopy model, run without above two options.

python spanCopyTrainer.py --gpus 1 \
                                --batch_size 4 \
                                --label_smoothing 0.1 \
                                --model_path /path/to/where/you/want/to/save/the/model \
                                --data_path /path/to/the/data/folder \
                                --model_name pegasus-cnndm \
                                --dataset_name cnndm \
                                --adafactor \
                                --use_global_relevance \
                                --beta 0.5 \          

To test the model

python spanCopyTrainer.py --mode test \
                                --resume_ckpt /path/to/the/checkpoint \
                                --gpus 1 \
                                --batch_size 4 \
                                --label_smoothing 0.1 \
                                --model_path /path/to/the/model/folder/where/you/want/to/save/summaries \
                                --data_path /path/to/the/data/folder \
                                --model_name pegasus-cnndm \
                                --dataset_name cnndm \
                                --use_global_relevance \
                                --beta 0.5 \
                                --save_gr

Pegasus bsl

To train/test the Pegasus model, simply run the pegasus_trainer.py with the same settings specified above.

Datasets

All the filtered/unfiltered datasets (CNNDM/Xsum/Pubmed/arXiv) can be found here.