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.
About this code
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 In particular, the code in attention_decoder.py is based on tf.contrib.legacy_seq2seq_attention_decoder, which is now outdated.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.
How to run
Get the dataset
To obtain the CNN / Daily Mail dataset, follow the instructions here. Once finished, you should have chunked datafiles
test_011.bin (each contains 1000 examples) and a vocabulary file
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
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_dec_steps) to something smaller than the defaults specified in
Increasing sequence length during training: Note that to obtain the results described in the paper, we increase the values of
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_dec_steps, then interrupt and restart the job with larger values when you want to increase them.
Run training with flip
By default training is done with "teacher forcing", instead of generating a new word and then feeding in that word as input when generating the next word, the expected word in the actual headline is fed in.
However, during decoding the previously generated word is fed in when
generating the next word. That leads to a disconnect between training
and testing. To overcome this disconnect, during training
you can set a random fraction of the steps to be replaced with
the predicted word of the previous step. You can do this with
You can increase
flip in a scheduled way. First train without any flip and then
increade flip to
For debugging, if you want to see what are all the predicted words for all steps, run with
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.
The eval job will also save a snapshot of the model that scored the lowest loss on the validation data so far.
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
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).
By default the beamsearch algorithm takes the best
--topk results but instead you can
specificy that the
topk result are randomly selected using
--temperature parameter. (e.g.
You can request multiple results to be generated for each article with
You can force the different trials to be different using
a penality for having a beam with same token as another beam. (https://arxiv.org/pdf/1610.02424.pdf)
Evaluate with ROUGE
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.