DyNet implementation of the paper Selective Encoding for Abstractive Sentence Summarization (ACL 2017)[1].

• Python 3.6.0+
• DyNet 2.0+
• NumPy 1.12.1+
• scikit-learn 0.19.0+
• tqdm 4.15.0+

To get gigaward corpus[2], run

sh download_giga.sh

.

• --gpu: GPU ID to use. For cpu, set -1 [default: 0]
• --n_epochs: Number of epochs [default: 3]
• --n_train: Number of training data (up to 3803957) [default: 3803957]
• --n_valid: Number of validation data (up to 189651) [default: 189651]
• --vocab_size: Vocabulary size [default: 124404]
• --batch_size: Mini batch size [default: 32]
• --emb_dim: Embedding size [default: 256]
• --hid_dim: Hidden state size [default: 256]
• --maxout_dim: Maxout size [default: 2]
• --alloc_mem: Amount of memory to allocate [mb] [default: 10000]

python train.py --n_epochs 20

• --gpu: GPU ID to use. For cpu, set -1 [default: 0]
• --n_test: Number of test data [default: 189651]
• --beam_size: Beam size [default: 5]
• --max_len: Maximum length of decoding [default: 100]
• --model_file: Trained model file path [default: ./model_e1]
• --input_file: Test file path [default: ./data/valid.article.filter.txt]
• --output_file: Output file path [default: ./pred_y.txt]
• --w2i_file: Word2Index file path [default: ./w2i.dump]
• --i2w_file: Index2Word file path [default: ./i2w.dump]
• --alloc_mem: Amount of memory to allocate [mb] [default: 1024]

python test.py --beam_size 10

You can use pythonrouge[3] to compute the ROUGE scores.

Work in progress.

Work in progress.

To get the pretrained model, run

sh download_pretrained_model.sh

.

• [1] Q. Zhou. et al. 2017. Selective Encoding for Abstractive Sentence Summarization. In Proceedings of ACL 2017 [pdf]
• [2] Gigaword/DUC2004 Corpus: https://github.com/harvardnlp/sent-summary
• [3] pythonrouge: https://github.com/tagucci/pythonrouge