zhou_acl_2017

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Selective Encoding for Abstractive Sentence Summarization

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

1. Requirement

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

2. Prepare dataset

To get gigaward corpus[2], run

sh download_giga.sh

.

3. Training

Arguments

• --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]

Command example

python train.py --n_epochs 20

4. Test

Arguments

• --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]

Command example

python test.py --beam_size 10

5. Evaluate

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

6. Results

6.1. Gigaword (validation data)

6.2. DUC2004

Work in progress.

6.3. MSR

Work in progress.

7. Pretrained model

To get the pretrained model, run

sh download_pretrained_model.sh

.

Notes

Reference

• [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