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https://github.com/nlpyang/PreSumm
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders
https://github.com/nlpyang/PreSumm
Last synced: 3 months ago
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code for EMNLP 2019 paper Text Summarization with Pretrained Encoders
- Host: GitHub
- URL: https://github.com/nlpyang/PreSumm
- Owner: nlpyang
- License: mit
- Created: 2019-08-15T13:37:21.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-05-06T06:56:15.000Z (over 1 year ago)
- Last Synced: 2024-06-14T01:49:12.339Z (5 months ago)
- Language: Python
- Size: 12.7 MB
- Stars: 1,275
- Watchers: 20
- Forks: 465
- Open Issues: 147
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PreSumm
**This code is for EMNLP 2019 paper [Text Summarization with Pretrained Encoders](https://arxiv.org/abs/1908.08345)**
**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. Please still use master branch for normal training and evaluation, dev branch should be only used for test_text mode.
* abstractive use -task abs, extractive use -task ext
* 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](https://github.com/nlpyang/PreSumm/tree/dev/raw_data)
* 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](https://drive.google.com/open?id=1kKWoV0QCbeIuFt85beQgJ4v0lujaXobJ)
[CNN/DM BertExtAbs](https://drive.google.com/open?id=1-IKVCtc4Q-BdZpjXc4s70_fRsWnjtYLr)
[CNN/DM TransformerAbs](https://drive.google.com/open?id=1yLCqT__ilQ3mf5YUUCw9-UToesX5Roxy)
[XSum BertExtAbs](https://drive.google.com/open?id=1H50fClyTkNprWJNh10HWdGEdDdQIkzsI)
## System Outputs
[CNN/DM and XSum](https://drive.google.com/file/d/1kYA384UEAQkvmZ-yWZAfxw7htCbCwFzC)
## Data Preparation For XSum
[Pre-processed data](https://drive.google.com/open?id=1BWBN1coTWGBqrWoOfRc5dhojPHhatbYs)
## Data Preparation For CNN/Dailymail
### Option 1: download the processed data
[Pre-processed data](https://drive.google.com/open?id=1DN7ClZCCXsk2KegmC6t4ClBwtAf5galI)
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](http://cs.nyu.edu/~kcho/DMQA/) 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](https://stanfordnlp.github.io/CoreNLP/) 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)