Structure-Aware-BART

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Structure-Aware-BART

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Source codes for "Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs"

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// repository documentation

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# Structure-Aware-BART
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.

## Getting Started
These instructions will get you running the codes of Structure-Aware-Bart Conversation Summarization.

### Requirements
* 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.**

### Install the transformers with S-BART

```
cd transformers

pip install --editable ./
```

### Downloading the data
Please download the SAMSUm dataset

### Pre-processing the data

The data folder you download from the above link already contains all the pre-processed files (including the extracted graphs) from SAMSum corpus.

#### Extract Discourse Graphs

Here we utilize the data and codes from [here](https://github.com/shizhouxing/DialogueDiscourseParsing) to pre-train a conversation discourse parser and use that parser to extract discourse graphs in the SAMSum dataset.

#### Extract Action Graphs

Please go through `./src/data/extract_actions.ipynb` to extract action graphs.

### Training models

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`

#### Training baseline BART model
Please run `./train_base.sh` to train the BART baseline models.

#### Training S-BART model
Please run `./train_multi_graph.sh` to train the S-BART model.

### Evaluating models

Please follow the example jupyter notebook (`./src/eval.ipynb`) is provided for evaluating the model on test set.

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