Source code for "Distraction-Based Neural Networks for Modeling Documents" runnable on GPU and CPU. If you use this code as part of any published research, please acknowledge the following paper.

"Distraction-Based Neural Networks for Modeling Documents"
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang. IJCAI (2016)

@InProceedings{Chen-Qian:2016:IJCAI,
  author    = {Chen, Qian and Zhu, Xiaodan and Ling, Zhenhua and Wei, Si and Jiang, Hui},
  title     = {Distraction-Based Neural Networks for Modeling Documents},
  booktitle = {Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2015)},
  month     = {July},
  year      = {2016},
  address   = {New York, NY},
  publisher = {AAAI}
}

Homepage of the Qian Chen, http:https://home.ustc.edu.cn/~cq1231/

This code is written in python. To use it you will need:

• Python 2.6/2.7
• NumPy
• scipy
• argparse
• Theano

cd data
python build_dictionary.py toy_train_input.txt

Some important path is needed to set in train_nats.py.

• datasets: training file of input and output
• valid_datasets: validation file of input and output
• dictionary: dictionary file
• model: saved model

If you don't have cuDNN, please comment the cuDNN configuation in train.sh.

cd scripts
bash train.sh

Some variable is needed to set in test.sh.

• KL: $\lambda_1$, the parameter of Kullback-Leibler (KL) divergence of attention weight vector
• CTX: $\lambda_2$, the parameter of Cosine distance of content vector
• STATE: $\lambda_3$, the parameter of Cosine distance of hidden state vector
• ROOT: root directory of directory
• MODEL: saved model
• DIC: dictionary file
• INPUT: test file of input
• TEMP: intermediate file of generated summary in testing set
• GEN: final file of generated summary in testing set
• REF: test file of reference summary

cd scripts
bash test.sh

• LCSTS: A Large-Scale Chinese Short Text Summarization Dataset
• CNN/DailyMail: This repository contains a script to download CNN and Daily Mail articles from the Wayback Machine.