li_emnlp_2017

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Deep Recurrent Generative Decoder for Abstractive Text Summarization

Unofficial DyNet implementation of the paper Deep Recurrent Generative Decoder for Abstractive Text Summarization (EMNLP 2017)[1]

1. Requirements

• 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 preprocedded gigaword corpus[2], run

sh download_gigaword_dataset.sh

3. Train

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: 60000]
• --batch_size: Mini batch size [default: 32]
• --emb_dim: Embedding size [default: 256]
• --hid_dim: Hidden state size [default: 256]
• --lat_dim: Latent state size [default: 256]
• --alloc_mem: Amount of memory to allocate [mb] [default: 8192]

Command example

python train.py --n_epochs 10

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[2] to measure the rouge scores.

6. Results

6.1. Gigaword (2000 validation data)

6.2. DUC 2004

Work in progress.

6.3. LCSTS

Work in progress.

7. Pretrained model

To get the pretrained model, run

sh download_gigaword_pretrained_model.sh

.

Notes

• ROUGE scores are much higher than the ones the paper reported, but I don't know why. Please tell me if you know why!
• Original paper lacks some details and notations, and some points do not make sense, so this implementation may be different from the original one.

References

• [1] P. Li et al. 2017. Deep Recurrent Generative Decoder for Abstractive Text Summarization. In Proceedings of EMNLP 2017 [pdf]
• [2] pythonrouge: https://github.com/tagucci/pythonrouge
• [3] Gigaword/DUC2004 Corpus: https://github.com/harvardnlp/sent-summary