# PreSumm **Repository Path**: halfskywalker/PreSumm ## Basic Information - **Project Name**: PreSumm - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-05-07 - **Last Updated**: 2025-05-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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)