Here we supprt online demo and video demonstration. Also, in this webpage we describe some critical research results briefly. For more detail, please contact with me by github or go to our paper as following:
- Python 3 (tested on python 3.6)
- PyTorch 1.1.0
- with GPU and CUDA enabled installation (though the code is runnable on CPU, it would be way too slow)
- gensim
- cytoolz
- tensorboardX
- pyrouge (for evaluation)
The code is tested on the Linux operating system.
To evaluate, you will need to download and setup the official ROUGE and METEOR packages.
We use pyrouge
(pip install pyrouge to install)
to make the ROUGE XML files required by the official perl script.
You will also need the official ROUGE package.
(However, it seems that the original ROUGE website is down.
An alternative can be found
here.)
Please specify the path to your ROUGE package by setting the environment variable
export ROUGE=[path/to/rouge/directory].
Download the pretrained models here.
You will also need a preprocessed version of the CNN/DailyMail dataset.
Please follow the instructions
here
for downloading and preprocessing the CNN/DailyMail dataset.
After that, specify the path of data files by setting the environment variable
export DATA=[path/to/decompressed/data]
To decode, run
python decode_full_model.py --path=[path/to/save/decoded/files] --model_dir=[path/to/pretrained] --beam=[beam_size] [--test/--val]
Options:
- beam_size: number of hypothesis for (diverse) beam search. (use beam_size > 1 to enable reranking)
- beam_szie=1 to get greedy decoding results (rnn-ext + abs + RL)
- beam_size=5 is used in the paper for the +rerank model (rnn-ext + abs + RL + rerank)
- test/val: decode on test/validation dataset
If you want to evaluate on the generated output files, please follow the instructions in the above section to setup ROUGE/METEOR.
Next, make the reference files for evaluation:
python make_eval_references.py
and then run evaluation by:
python eval_full_model.py --[rouge/meteor] --decode_dir=[path/to/save/decoded/files]
You should get the following results
Test set
| Models | ROUGEs (R-1, R-2, R-L) |
|---|---|
| ext + abs | (40.02, 17.53, 37.46) |
| +absAgent | (40.79, 18.53, 37.92) |
| + rerank | (41.39, 18.74, 38.51) |
NOTE: The original models in the paper are trained with pytorch 1.1.0 on python 3.
Please follow the instructions
here
for downloading and preprocessing the CNN/DailyMail dataset.
After that, specify the path of data files by setting the environment variable
export DATA=[path/to/decompressed/data]
To re-train our best model:
- pretrained a word2vec word embedding
python train_word2vec.py --path=[path/to/word2vec]
- make the pseudo-labels
python make_extraction_labels.py
- train abstractor and extractor using ML objectives
python train_abstractor.py --path=[path/to/abstractor/model] --w2v=[path/to/word2vec/word2vec.128d.226k.bin]
python train_extractor_ml.py --path=[path/to/extractor/model] --w2v=[path/to/word2vec/word2vec.128d.226k.bin]
- train the full RL model
python train_full_rl.py --path=[path/to/save/model] --abs_dir=[path/to/abstractor/model] --ext_dir=[path/to/extractor/model]
After the training finishes you will be able to run the decoding and evaluation following the instructions in the previous section.
The above will use the best hyper-parameters we used in the paper as default. Please refer to the respective source code for options to set the hyper-parameters.
Thanks to the open source provided from Yen-Chun Chen and Mohit Bansal that assists us to fininsh the whole research. We appreciate and show the reference below:
@inproceedings{chen2018fast,
title={Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting},
author={Yen-Chun Chen and Mohit Bansal},
booktitle={Proceedings of ACL},
year={2018}
}