/PreSumm

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

Primary LanguagePythonMIT LicenseMIT

PreSumm

This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders

Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swith to the dev branch, and use -mode test_text and use -text_src $RAW_SRC.TXT to input your text file.

  • use -test_from $PT_FILE$ to use your model checkpoint file.
  • Format of the source text file:
    • For abstractive summarization, each line is a document.
    • If you want to do extractive summarization, please insert [CLS] [SEP] as your sentence boundaries.
  • There are example input files in the raw_data directory
  • If you also have reference summaries aligned with your source input, please use -text_tgt $RAW_TGT.TXT to keep the order for evaluation.

Results on CNN/DailyMail (20/8/2019):

Models ROUGE-1 ROUGE-2 ROUGE-L
Extractive
TransformerExt 40.90 18.02 37.17
BertSumExt 43.23 20.24 39.63
BertSumExt (large) 43.85 20.34 39.90
Abstractive
TransformerAbs 40.21 17.76 37.09
BertSumAbs 41.72 19.39 38.76
BertSumExtAbs 42.13 19.60 39.18

Python version: This code is in Python3.6

Package Requirements: torch==1.1.0 pytorch_transformers tensorboardX multiprocess pyrouge

Updates: For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

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

Trained Models

CNN/DM BertExt

CNN/DM BertExtAbs

CNN/DM TransformerAbs

XSum BertExtAbs

System Outputs

CNN/DM and XSum

Data Preparation For XSum

Pre-processed data

Data Preparation For CNN/Dailymail

Option 1: download the processed data

Pre-processed data

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 -n_cpus 1 -use_bert_basic_tokenizer false -map_path MAP_PATH
  • 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  -lower -n_cpus 1 -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)

Model Training

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

Extractive Setting

python train.py -task ext -mode train -bert_data_path BERT_DATA_PATH -ext_dropout 0.1 -model_path MODEL_PATH -lr 2e-3 -visible_gpus 0,1,2 -report_every 50 -save_checkpoint_steps 1000 -batch_size 3000 -train_steps 50000 -accum_count 2 -log_file ../logs/ext_bert_cnndm -use_interval true -warmup_steps 10000 -max_pos 512

Abstractive Setting

TransformerAbs (baseline)

python train.py -mode train -accum_count 5 -batch_size 300 -bert_data_path BERT_DATA_PATH -dec_dropout 0.1 -log_file ../../logs/cnndm_baseline -lr 0.05 -model_path MODEL_PATH -save_checkpoint_steps 2000 -seed 777 -sep_optim false -train_steps 200000 -use_bert_emb true -use_interval true -warmup_steps 8000  -visible_gpus 0,1,2,3 -max_pos 512 -report_every 50 -enc_hidden_size 512  -enc_layers 6 -enc_ff_size 2048 -enc_dropout 0.1 -dec_layers 6 -dec_hidden_size 512 -dec_ff_size 2048 -encoder baseline -task abs

BertAbs

python train.py  -task abs -mode train -bert_data_path BERT_DATA_PATH -dec_dropout 0.2  -model_path MODEL_PATH -sep_optim true -lr_bert 0.002 -lr_dec 0.2 -save_checkpoint_steps 2000 -batch_size 140 -train_steps 200000 -report_every 50 -accum_count 5 -use_bert_emb true -use_interval true -warmup_steps_bert 20000 -warmup_steps_dec 10000 -max_pos 512 -visible_gpus 0,1,2,3  -log_file ../logs/abs_bert_cnndm

BertExtAbs

python train.py  -task abs -mode train -bert_data_path BERT_DATA_PATH -dec_dropout 0.2  -model_path MODEL_PATH -sep_optim true -lr_bert 0.002 -lr_dec 0.2 -save_checkpoint_steps 2000 -batch_size 140 -train_steps 200000 -report_every 50 -accum_count 5 -use_bert_emb true -use_interval true -warmup_steps_bert 20000 -warmup_steps_dec 10000 -max_pos 512 -visible_gpus 0,1,2,3 -log_file ../logs/abs_bert_cnndm  -load_from_extractive EXT_CKPT   
  • EXT_CKPT is the saved .pt checkpoint of the extractive model.

Model Evaluation

CNN/DM

 python train.py -task abs -mode validate -batch_size 3000 -test_batch_size 500 -bert_data_path BERT_DATA_PATH -log_file ../logs/val_abs_bert_cnndm -model_path MODEL_PATH -sep_optim true -use_interval true -visible_gpus 1 -max_pos 512 -max_length 200 -alpha 0.95 -min_length 50 -result_path ../logs/abs_bert_cnndm 

XSum

 python train.py -task abs -mode validate -batch_size 3000 -test_batch_size 500 -bert_data_path BERT_DATA_PATH -log_file ../logs/val_abs_bert_cnndm -model_path MODEL_PATH -sep_optim true -use_interval true -visible_gpus 1 -max_pos 512 -min_length 20 -max_length 100 -alpha 0.9 -result_path ../logs/abs_bert_cnndm 
  • -mode can be {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_PATH is the directory of saved checkpoints
  • use -mode valiadte with -test_all, the system will load all saved checkpoints and select the top ones to generate summaries (this will take a while)