This repo contains codes for the following paper:
Jiaao Chen, Diyi Yang:Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs, NAACL 2021
If you would like to refer to it, please cite the paper mentioned above.
These instructions will get you running the codes of Structure-Aware-Bart Conversation Summarization.
- Python 3.6 or higher
- Pytorch >= 1.3.0
- Pandas, Numpy, Pickle
- rouge=1.0.0 (https://github.com/pltrdy/rouge)
- transformers
- allennlp
- openie
- wandb
Note that different versions of rouge or different rouge packages might result in different rouge scores. For the transformers, we used the version released by Oct. 7 2020. The updated version might also result in different performances.
cd transformers
pip install --editable ./
Please download the SAMSUm dataset
The data folder you download from the above link already contains all the pre-processed files (including the extracted graphs) from SAMSum corpus.
Here we utilize the data and codes from here to pre-train a conversation discourse parser and use that parser to extract discourse graphs in the SAMSum dataset.
Please go through ./src/data/extract_actions.ipynb to extract action graphs.
These section contains instructions for training the conversation summarizationmodels.
The generated summaries on test set for baseline BART and the S-BART is in the ./src/baseline and ./src/composit folder. (trained with seed 42)
The training logs from wandb for different seed (0,1,42) for S-BART is shown in ./src/Weights&Biases.pdf
Please run ./train_base.sh to train the BART baseline models.
Please run ./train_multi_graph.sh to train the S-BART model.
Please follow the example jupyter notebook (./src/eval.ipynb) is provided for evaluating the model on test set.