The pointer generator is a deep neural network built for abstractive summarizations. For more informations on this model, https://arxiv.org/pdf/1704.04368
With my collaborator Kevin Sylla , we re-made this model in tensorflow for our research project. This neural net will be our baseline model. We will do some experiments with this model, and propose a new architecture based on this one.
In this project, you can:
- train models
- test ²
- evaluate ²
This project reads tfrecords format files. For our experiments, we will be working on the ccn and dailymail datasets. You can download the preprocessed files with this link : https://github.com/JafferWilson/Process-Data-of-CNN-DailyMail
Or do the pre-processing by yourself with this link : https://github.com/abisee/cnn-dailymail
You may launch the program with the following command: (have a look at the main.py script for more informations about the attributes)
python main.py
--max_enc_len=400
--max_dec_len=100
--max_dec_steps=120
--min_dec_steps=30
--batch_size=4
--beam_size=4
--vocab_size=50000
--embed_size=128
--enc_units=256
--dec_units=256
--attn_units=512
--learning_rate=0.15
--adagrad_init_acc=0.1
--max_grad_norm=0.8
--mode="eval"
--checkpoints_save_steps=5000
--max_steps=38000
--num_to_test=5
--max_num_to_eval=100
--vocab_path="../../Datasets/tfrecords_folder/tfrecords_folder/vocab"
--data_dir="../../Datasets/tfrecords_folder/tfrecords_folder/val"
--model_path="../pgn_model_dir/checkpoint/ckpt-37000"
--checkpoint_dir="../pgn_model_dir/checkpoint"
--test_save_dir="../pgn_model_dir/test_dir/"