This repository contains code for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks. The test set output of the models described in the paper can be found here.
This code is based on the TextSum code 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 In particular, the code in attention_decoder.py is based on tf.contrib.legacy_seq2seq_attention_decoder, which is now outdated. Tensorflow 1.0's new seq2seq library probably provides a way to do this (as well as beam search) more elegantly and efficiently in the future.
To obtain the CNN / Daily Mail dataset, follow the instructions here. Once finished, you should have chunked 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 to correct a bug in the process.
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.
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.
The eval job will also save a snapshot of the model that scored the lowest loss on the validation data so far.
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.
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.
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. (Note this will not produce the attn_vis_data.json files for the attention visualizer).
decode.py uses the Python package 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:
Note: As of 18th May 2017 the website 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 (however, the version of pyrouge in that repo appears to be outdated, so best to install pyrouge from the official source).
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.