### Get Preprocessed CNN Data
To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:

```bash
wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
tar -xzvf cnn_dm.tgz
```

this should make a directory called cnn_dm/ with files like `test.source`. 
To use your own data, copy that files format. Each article to be summarized is on its own line.

### Evaluation
To create summaries for each article in dataset, run:
```bash
python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
```
the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.


### Training
Run/modify `run_train.sh`
    
### Where is the code?
The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.

## (WIP) Rouge Scores

### Stanford CoreNLP Setup
```
ptb_tokenize () {
    cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
}

sudo apt install openjdk-8-jre-headless
sudo apt-get install ant
wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
unzip stanford-corenlp-full-2018-10-05.zip
cd stanford-corenlp-full-2018-10-05
export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
```
Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
### Rouge Setup
Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
I also needed to run `sudo apt-get install libxml-parser-perl`

```python
from files2rouge import files2rouge
from files2rouge import settings
files2rouge.run(<path_to_tokenized_hypo>,
                <path_to_tokenized_target>,
               saveto='rouge_output.txt')
```