BertSum

This code is for paper Fine-tune BERT for Extractive Summarization(https://arxiv.org/pdf/1903.10318.pdf)

!New: Please see our full paper with trained models

Results on CNN/Dailymail (25/3/2019):

Models	ROUGE-1	ROUGE-2	ROUGE-L
Transformer Baseline	40.9	18.02	37.17
BERTSUM+Classifier	43.23	20.22	39.60
BERTSUM+Transformer	43.25	20.24	39.63
BERTSUM+LSTM	43.22	20.17	39.59

Python version: This code is in Python3.6

Package Requirements: pytorch pytorch_pretrained_bert tensorboardX multiprocess pyrouge

Some codes are borrowed from ONMT(https://github.com/OpenNMT/OpenNMT-py)

Data Preparation For CNN/Dailymail

Option 1: download the processed data

download https://drive.google.com/open?id=1x0d61LP9UAN389YN00z0Pv-7jQgirVg6

unzip the zipfile and put all .pt files into bert_data

Option 2: process the data yourself

Step 1 Download Stories

Download and unzip the stories directories from here for both CNN and Daily Mail. Put all .story files in one directory (e.g. ../raw_stories)

Step 2. Download Stanford CoreNLP

We will need Stanford CoreNLP to tokenize the data. Download it here and unzip it. Then add the following command to your bash_profile:

export CLASSPATH=/path/to/stanford-corenlp-full-2017-06-09/stanford-corenlp-3.8.0.jar

replacing /path/to/ with the path to where you saved the stanford-corenlp-full-2017-06-09 directory.

Step 3. Sentence Splitting and Tokenization

python preprocess.py -mode tokenize -raw_path RAW_PATH -save_path TOKENIZED_PATH

RAW_PATH is the directory containing story files (../raw_stories), JSON_PATH is the target directory to save the generated json files (../merged_stories_tokenized)

Step 4. Format to Simpler Json Files

python preprocess.py -mode format_to_lines -raw_path RAW_PATH -save_path JSON_PATH -map_path MAP_PATH -lower

RAW_PATH is the directory containing tokenized files (../merged_stories_tokenized), JSON_PATH is the target directory to save the generated json files (../json_data/cnndm), MAP_PATH is the directory containing the urls files (../urls)

Step 5. Format to PyTorch Files

python preprocess.py -mode format_to_bert -raw_path JSON_PATH -save_path BERT_DATA_PATH -oracle_mode greedy -n_cpus 4 -log_file ../logs/preprocess.log

JSON_PATH is the directory containing json files (../json_data), BERT_DATA_PATH is the target directory to save the generated binary files (../bert_data)
-oracle_mode can be greedy or combination, where combination is more accurate but takes much longer time to process

Model Training

First run: For the first time, you should use single-GPU, so the code can download the BERT model. Change -visible_gpus 0,1,2 -gpu_ranks 0,1,2 -world_size 3 to -visible_gpus 0 -gpu_ranks 0 -world_size 1, after downloading, you could kill the process and rerun the code with multi-GPUs.

To train the BERT+Classifier model, run:

python train.py -mode train -encoder classifier -dropout 0.1 -bert_data_path ../bert_data/cnndm -model_path ../models/bert_classifier -lr 2e-3 -visible_gpus 0,1,2  -gpu_ranks 0,1,2 -world_size 3 -report_every 50 -save_checkpoint_steps 1000 -batch_size 3000 -decay_method noam -train_steps 50000 -accum_count 2 -log_file ../logs/bert_classifier -use_interval true -warmup_steps 10000

To train the BERT+Transformer model, run:

python train.py -mode train -encoder transformer -dropout 0.1 -bert_data_path ../bert_data/cnndm -model_path ../models/bert_transformer -lr 2e-3 -visible_gpus 0,1,2  -gpu_ranks 0,1,2 -world_size 3 -report_every 50 -save_checkpoint_steps 1000 -batch_size 3000 -decay_method noam -train_steps 50000 -accum_count 2 -log_file ../logs/bert_transformer -use_interval true -warmup_steps 10000 -ff_size 2048 -inter_layers 2 -heads 8

To train the BERT+RNN model, run:

python train.py -mode train -encoder rnn -dropout 0.1 -bert_data_path ../bert_data/cnndm -model_path ../models/bert_rnn -lr 2e-3 -visible_gpus 0,1,2  -gpu_ranks 0,1,2 -world_size 3 -report_every 50 -save_checkpoint_steps 1000 -batch_size 3000 -decay_method noam -train_steps 50000 -accum_count 2 -log_file ../logs/bert_rnn -use_interval true -warmup_steps 10000 -rnn_size 768 -dropout 0.1

-mode can be {train, validate, test}, where validate will inspect the model directory and evaluate the model for each newly saved checkpoint, test need to be used with -test_from, indicating the checkpoint you want to use

Model Evaluation

After the training finished, run

python train.py -mode validate -bert_data_path ../bert_data/cnndm -model_path MODEL_PATH  -visible_gpus 0  -gpu_ranks 0 -batch_size 30000  -log_file LOG_FILE  -result_path RESULT_PATH -test_all -block_trigram true

MODEL_PATH is the directory of saved checkpoints
RESULT_PATH is where you want to put decoded summaries (default ../results/cnndm)

Maskyyds/BertSum