Main differences from the Santosh-Gupta BertSum repo:

Converting formulae .xml files to .story

e.g.

python xml_story_converter.py -raw_path ../raw_data/xml/schaeftlarn -save_path ../raw_data/stories/la -log_file ../logs/converting.log -verbose true

e.g.

python src/xml_story_converter.py -raw_path ./raw_data/xml -save_path ./raw_data/stories/la_de -log_file ./logs/converting.log

Adding a translation layer:

python src/translate.py -src_language la -dest_language de -mode pre_highlight -raw_path ./raw_data/stories/la_de -save_path ./raw_data/stories/de_de -log_file ./logs/translate.log

Usage of Stanza instead of CoreNLP for multilingual tokenizing:

python preprocess.py -mode tokenize_stanza -raw_path ../raw_data/stories/de -save_path ../raw_data/merged_stories_tokenized/de -log_file ../logs/tokenize_testing.log -language de

Generalized format_to_linesMS2 to make it usable in Windows:

python preprocess.py -mode format_to_linesMS2 -raw_path ../raw_data/merged_stories_tokenized/de -save_path ../json_data/de -log_file ../logs/format_to_lines_testing.log

Generalized format_to_bertMS to make it usable in Windows:

python preprocess.py -mode format_to_bertMS -raw_path ../json_data -save_path ../bert_data -oracle_mode greedy -n_cpus 1 -log_file ../logs/format_to_bert_testing.log

Main differences from the original BertSum repo.

The original Bertsum repo was designed for the CNN/DM datasets. The following are changes I made in order to effectivly process arxiv papers.

-Files to be tokenized should now end with '.y' instead of ',story'. This is because the stanford tokenizer has limits to the file name size, so this is to reduce file name size as much as possibe.

-Enable to load different types of pre-trained weights for initial training, outside pretrained weights choices in the Huggingface Transformers. The purpose of this is to be able to use Allen AI's SciBert, which are Bert weights trained on scientific literature.

https://github.com/Santosh-Gupta/BertSum/blob/master/src/models/model_builder.py#L43

-Enable different tokenizer vocab files to be used.

https://github.com/Santosh-Gupta/BertSum/blob/master/src/prepro/data_builder.py#L149

-Keep track of the ArxivID of sections summarized

https://github.com/Santosh-Gupta/BertSum/blob/master/src/prepro/data_builder.py#L428

-Different type of testing modes, producing CSV files which can compare the summaries to the original text, and easily opened in Pandas or any other CSV reader. https://github.com/Santosh-Gupta/BertSum/blob/master/src/train.py#L244

https://github.com/Santosh-Gupta/BertSum/blob/master/src/models/trainer.py#L498

-Preprocess files which only contain one summary. The original BertSum repo was designed for the CNN/DM dataset, which contains several lines for summaries, each following a @highlight token. I changed the preprocessing to process datasamples which only have one summary per source https://github.com/Santosh-Gupta/BertSum/blob/master/src/prepro/data_builder.py#L30

BertSum

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

Please email the author for a pre-trained model

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)