toru34 / rush_emnlp_2015

A Neural Attention Model for Abstractive Sentence Summarization in DyNet

A Neural Attention Model for Abstractive Sentence Summarization

Unofficial DyNet implementation of the paper A Neural Attention Model for Abstractive Sentence Summarization (EMNLP 2015)[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, run

sh download_data.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]
• --batch_size: Mini batch size [default: 32]
• --vocab_size: Vocabulary size [default: 60000]
• --emb_dim: Embedding size [default: 256]
• --hid_dim: Hidden state size [default: 256]
• --encoder_type: Encoder type. [default: attention]
  • bow: Bag-of-words encoder.
  • attention: Attention-based encoder.
• --c: Window size in neural language model [default: 5]
• --q: Window size in attention-based encoder [default: 2]
• --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 compute the ROUGE scores. An example is in evaluate.ipynb.

6. Results

6.1. Gigaword

Compute ROUGE scorew with 101 valid data.

	ROUGE-1 (F1)	ROUGE-2 (F1)	ROUGE-L (F1)
My implementation	31.56	14.56	30.02

6.2. DUC2004

Work in progress.

7. Pretrained model

To get the pretrained model, run

sh download_pretrained_model.sh

.

Notes

• Convolutional encoder is not implemented.
• Extractive tuning is not implemented.
• Official code: https://github.com/facebookarchive/NAMAS

References

• [1] A. M. Rush et al. 2015. A Neural Attention Model for Sentence Summarization. In Proceedings of EMNLP 2015 [pdf]
• [2] pythonrouge: https://github.com/tagucci/pythonrouge