Codes and datasets for our paper: Neural Review Summarization Leveraging User and Product Information (CIKM19)
- python >= 3.5
- pytorch >= 1.1.0
- sumeval
- tqdm
Download the datasets from Google Drive or Baidu Pan. Put the unziped /data directory into the project root directory.
The repository contains three baseline models: seq2seq, seq2seqAttn, pgn, and our proposed four models: AttrEnc, AttrDec, AttrEncDec, and MemAttr, as mentioned in our paper. The command to run each model is the same. Take our novel model MemAttr as an example:
Train a MemAttr model:
cd code/memAttr
python train.py
Test the trained model:
python train.py -test -load_model <the_checkpoint_you_want_to_test>
The train and test parameters can be found in the source code train.py.
If you use our codes or datasets in your research, please kindly cite our paper: Neural Review Summarization Leveraging User and Product Information (CIKM19)