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https://github.com/abisee/pointer-generator

Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"
https://github.com/abisee/pointer-generator

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Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"

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README 

        
*Note: this code is no longer actively maintained. However, feel free to use the Issues section to discuss the code with other users. Some users have updated this code for newer versions of Tensorflow and Python - see information below and Issues section.*



---



This repository contains code for the ACL 2017 paper *[Get To The Point: Summarization with Pointer-Generator Networks](https://arxiv.org/abs/1704.04368)*. For an intuitive overview of the paper, read the [blog post](http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html).



## Looking for test set output?

The test set output of the models described in the paper can be found [here](https://drive.google.com/file/d/0B7pQmm-OfDv7MEtMVU5sOHc5LTg/view?usp=sharing).



## Looking for pretrained model?

A pretrained model is available here:

* [Version for Tensorflow 1.0](https://drive.google.com/open?id=0B7pQmm-OfDv7SHFadHR4RllfR1E)

* [Version for Tensorflow 1.2.1](https://drive.google.com/open?id=0B7pQmm-OfDv7ZUhHZm9ZWEZidDg)



(The only difference between these two is the naming of some of the variables in the checkpoint. Tensorflow 1.0 uses `lstm_cell/biases` and `lstm_cell/weights` whereas Tensorflow 1.2.1 uses `lstm_cell/bias` and `lstm_cell/kernel`).



**Note**: This pretrained model is *not* the exact same model that is reported in the paper. That is, it is the same architecture, trained with the same settings, but resulting from a different training run. Consequently this pretrained model has slightly lower ROUGE scores than those reported in the paper. This is probably due to us slightly overfitting to the randomness in our original experiments (in the original experiments we tried various hyperparameter settings and selected the model that performed best). Repeating the experiment once with the same settings did not perform quite as well. Better results might be obtained from further hyperparameter tuning.



**Why can't you release the trained model reported in the paper?** Due to changes to the code between the original experiments and the time of releasing the code (e.g. TensorFlow version changes, lots of code cleanup), it is not possible to release the original trained model files. 



## Looking for CNN / Daily Mail data?

Instructions are [here](https://github.com/abisee/cnn-dailymail).



## About this code

This code is based on the [TextSum code](https://github.com/tensorflow/models/tree/master/textsum) from Google Brain.



This code was developed for Tensorflow 0.12, but has been updated to run with Tensorflow 1.0.

In particular, the code in attention_decoder.py is based on [tf.contrib.legacy_seq2seq_attention_decoder](https://www.tensorflow.org/api_docs/python/tf/contrib/legacy_seq2seq/attention_decoder), which is now outdated.

Tensorflow 1.0's [new seq2seq library](https://www.tensorflow.org/api_guides/python/contrib.seq2seq#Attention) probably provides a way to do this (as well as beam search) more elegantly and efficiently in the future.



**Python 3 version**: This code is in Python 2. If you want a Python 3 version, see [@becxer's fork](https://github.com/becxer/pointer-generator/).



## How to run



### Get the dataset

To obtain the CNN / Daily Mail dataset, follow the instructions [here](https://github.com/abisee/cnn-dailymail). Once finished, you should have [chunked](https://github.com/abisee/cnn-dailymail/issues/3) datafiles `train_000.bin`, ..., `train_287.bin`, `val_000.bin`, ..., `val_013.bin`, `test_000.bin`, ..., `test_011.bin` (each contains 1000 examples) and a vocabulary file `vocab`.



**Note**: If you did this before 7th May 2017, follow the instructions [here](https://github.com/abisee/cnn-dailymail/issues/2) to correct a bug in the process.



### Run training

To train your model, run:



```

python run_summarization.py --mode=train --data_path=/path/to/chunked/train_* --vocab_path=/path/to/vocab --log_root=/path/to/a/log/directory --exp_name=myexperiment

```



This will create a subdirectory of your specified `log_root` called `myexperiment` where all checkpoints and other data will be saved. Then the model will start training using the `train_*.bin` files as training data.



**Warning**: Using default settings as in the above command, both initializing the model and running training iterations will probably be quite slow. To make things faster, try setting the following flags (especially `max_enc_steps` and `max_dec_steps`) to something smaller than the defaults specified in `run_summarization.py`: `hidden_dim`, `emb_dim`, `batch_size`, `max_enc_steps`, `max_dec_steps`, `vocab_size`. 



**Increasing sequence length during training**: Note that to obtain the results described in the paper, we increase the values of `max_enc_steps` and `max_dec_steps` in stages throughout training (mostly so we can perform quicker iterations during early stages of training). If you wish to do the same, start with small values of `max_enc_steps` and `max_dec_steps`, then interrupt and restart the job with larger values when you want to increase them.



### Run (concurrent) eval

You may want to run a concurrent evaluation job, that runs your model on the validation set and logs the loss. To do this, run:



```

python run_summarization.py --mode=eval --data_path=/path/to/chunked/val_* --vocab_path=/path/to/vocab --log_root=/path/to/a/log/directory --exp_name=myexperiment

```



Note: you want to run the above command using the same settings you entered for your training job.



**Restoring snapshots**: The eval job saves a snapshot of the model that scored the lowest loss on the validation data so far. You may want to restore one of these "best models", e.g. if your training job has overfit, or if the training checkpoint has become corrupted by NaN values. To do this, run your train command plus the `--restore_best_model=1` flag. This will copy the best model in the eval directory to the train directory. Then run the usual train command again.



### Run beam search decoding

To run beam search decoding:



```

python run_summarization.py --mode=decode --data_path=/path/to/chunked/val_* --vocab_path=/path/to/vocab --log_root=/path/to/a/log/directory --exp_name=myexperiment

```



Note: you want to run the above command using the same settings you entered for your training job (plus any decode mode specific flags like `beam_size`).



This will repeatedly load random examples from your specified datafile and generate a summary using beam search. The results will be printed to screen.



**Visualize your output**: Additionally, the decode job produces a file called `attn_vis_data.json`. This file provides the data necessary for an in-browser visualization tool that allows you to view the attention distributions projected onto the text. To use the visualizer, follow the instructions [here](https://github.com/abisee/attn_vis).



If you want to run evaluation on the entire validation or test set and get ROUGE scores, set the flag `single_pass=1`. This will go through the entire dataset in order, writing the generated summaries to file, and then run evaluation using [pyrouge](https://pypi.python.org/pypi/pyrouge). (Note this will *not* produce the `attn_vis_data.json` files for the attention visualizer).



### Evaluate with ROUGE

`decode.py` uses the Python package [`pyrouge`](https://pypi.python.org/pypi/pyrouge) to run ROUGE evaluation. `pyrouge` provides an easier-to-use interface for the official Perl ROUGE package, which you must install for `pyrouge` to work. Here are some useful instructions on how to do this:

* [How to setup Perl ROUGE](http://kavita-ganesan.com/rouge-howto)

* [More details about plugins for Perl ROUGE](http://www.summarizerman.com/post/42675198985/figuring-out-rouge)



**Note:** As of 18th May 2017 the [website](http://berouge.com/) for the official Perl package appears to be down. Unfortunately you need to download a directory called `ROUGE-1.5.5` from there. As an alternative, it seems that you can get that directory from [here](https://github.com/andersjo/pyrouge) (however, the version of `pyrouge` in that repo appears to be outdated, so best to install `pyrouge` from the [official source](https://pypi.python.org/pypi/pyrouge)).



### Tensorboard

Run Tensorboard from the experiment directory (in the example above, `myexperiment`). You should be able to see data from the train and eval runs. If you select "embeddings", you should also see your word embeddings visualized.



### Help, I've got NaNs!

For reasons that are [difficult to diagnose](https://github.com/abisee/pointer-generator/issues/4), NaNs sometimes occur during training, making the loss=NaN and sometimes also corrupting the model checkpoint with NaN values, making it unusable. Here are some suggestions:



* If training stopped with the `Loss is not finite. Stopping.` exception, you can just try restarting. It may be that the checkpoint is not corrupted.

* You can check if your checkpoint is corrupted by using the `inspect_checkpoint.py` script. If it says that all values are finite, then your checkpoint is OK and you can try resuming training with it.

* The training job is set to keep 3 checkpoints at any one time (see the `max_to_keep` variable in `run_summarization.py`). If your newer checkpoint is corrupted, it may be that one of the older ones is not. You can switch to that checkpoint by editing the `checkpoint` file inside the `train` directory.

* Alternatively, you can restore a "best model" from the `eval` directory. See the note **Restoring snapshots** above.

* If you want to try to diagnose the cause of the NaNs, you can run with the `--debug=1` flag turned on. This will run [Tensorflow Debugger](https://www.tensorflow.org/versions/master/programmers_guide/debugger), which checks for NaNs and diagnoses their causes during training.