Paper: "Post-Training Dialogue Summarization using Pseudo-Paraphrasing" accepted by Findings of NAACL 2022. Enhancing dialogue summarization by post-training with pseudo-paraphrase pairs and prefix-guided generation task.
Requirements
- python3.7
- pytorch1.7.0
- transformers 4.7.0
Post-training and Fine-tuning
-
Replace the original "modeling_bart.py" under "transformers/src/transformers/models/bart" with the "modeling_bart.py" we provided.
-
Download the data and put it into corresponding directories.
-
Run the post-training processes with scripts under ./experiment_scripts.
bash posttrain_dialsumm_exact.sh
bash posttrain_samsum_exact.sh
- Modify MODEL_DIR with the path of post-trained checkpoints of scripts under ./experiment_scripts and run the fine-tuning processes.
bash finetune_dialsumm.sh
bash finetune_samsum.sh
Best results listed in the paper can be downloaded here.