Controllable Summarization with Constrained Markov Decision Process

This repository contains the source code for our TACL paper "Controllable Summarization with Constrained Markov Decision Process".

Some of our code are adapted from Huggingface Transformers, Fast Abstractive Summarization-RL, and summa-qa.

If you use our code, please cite our paper:

@article{DBLP:journals/tacl/cmdp-control-sum-21,
  author    = {Hou Pong Chan and Lu Wang and Irwin King},
  title     = {Controllable Summarization with Constrained Markov Decision Process},
  journal   = {Transactions of the Association for Computational Linguistics},
  year      = {2021}
}

Dependencies

python==3.7
pytorch==1.4.0
transformers==2.5.0
bert-score==0.3.1
moverscore==1.0.3
gensim
cytoolz
pyrouge
ntlk
tqdm
rreplace
sklearn
tensorflow==2.1.0 (for tensorboard)
tensorboardx
spacy==2.1.9
neuralcoref

Datasets

You can download the preprocessed datasets as follows

CNN/DM, for length and abstractiveness control.
Newsroom-b
CNN/DM, for entity control, the data for training the QA model of QAF1 score is located in the cloze_entity_squad_with_idx_and_unanswerable_and_paraphrase_and_repeat folder.
For the DUC-2002 dataset, please sign the agreements and request the DUC-2002 dataset follows the instructions here. After you obtain their approval, please send an email to me ([email protected]) to request our preprocessed version of DUC-2002.

Length Control

Training

ML training for D.GPT2 model on CNN/DM.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_finetuning.py \
--data_dir ../../datasets/cased-cnn-dailymail \
--output_dir saved_models/train_ml_distilgpt2_cnn_512_control_mode_1 \
--model_type gpt2 \
--model_name_or_path distilgpt2 \
--tokenizer_name distilgpt2 \
--cache_dir /data/model_cache \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 6 \
--per_gpu_eval_batch_size 16 \
--learning_rate 5e-5 \
--logging_steps 5000 \
--seed 9527 \
--num_train_epochs 12 \
--max_epochs 12 \
--control_modes 1 \
--gradient_accumulation_steps 3 \
--input_trunc_length 512 \
--fp16

CMDP training for D.GPT2 model on CNN/DM.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_rl_finetuning.py \
--data_dir ../../datasets/cased-cnn-dailymail \
--output_dir saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_1_512 \
--model_type gpt2 \
--model_name_or_path saved_models/train_ml_distilgpt2_cnn_512_control_mode_1/epoch_states/10-epoch \
--tokenizer_name saved_models/train_ml_distilgpt2_cnn_512_control_mode_1 \
--cache_dir /data/model_cache \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 12 \
--per_gpu_eval_batch_size 24 \
--learning_rate 1.77e-5 \
--save_total_limit 1 \
--max_output_length 90 \
--seed 9527 \
--num_train_epochs 20 \
--reward_type 9 \
--baseline self \
--constrained_mdp \
--cost_types 13 15 \
--cost_thresholds 0.0 0.0 \
--lagrangian_init_val 0.01 \
--logging_steps 1000 \
--control_modes 1 \
--gradient_accumulation_steps 3 \
--fp16 \
--num_freeze_layers 4

Testing

Download pyrouge, and save it to path/to/pyrouge. git clone https://github.com/andersjo/pyrouge.git
Export ROUGE score environment variable export ROUGE=[path/to/pyrouge/tools/ROUGE-1.5.5]
Make evaluation reference for CNN/DM and DUC2002 datasets (only need to do it for once for each dataset) python make_eval_reference.py -data ../../datasets/cased-cnn-dailymail -split test python make_eval_reference_duc.py -data ../../datasets/duc2002_preprocessed_long -split test

Reference length bin control

Decode from D.GPT2 model using reference length bins on CNN/DM. Specify the model_name_or_path to the best checkpoint (highest validation reward).

python3 -u gpt2_summarization_prediction.py \
--model_type gpt2 \
--model_name_or_path saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_1_512/epoch_states/18-epoch \
--tokenizer_name saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_1_512 \
--pred_path pred/predict_rl_distilgpt2_cnn_512_control_mode_1 \
--data_dir ../../datasets/cased-cnn-dailymail \
--split test \
--temperature 1.0 \
--batch_size 8 \
--beam_size 5 \
--control_modes 1 \
--with_ground_truth_input \
--seed 9527 \
--input_trunc_length 512

Compute ROUGE scores

python evaluate_prediction.py -rouge -decode_dir pred/predict_rl_distilgpt2_cnn_512_control_mode_1 -data ../../datasets/cased-cnn-dailymail

Compute bin %

python output_len_stat.py -decode_dir pred/predict_rl_distilgpt2_cnn_512_control_mode_1 -data_dir ../../datasets/cased-cnn-dailymail -split test

Arbitrary length bin control

Decode from D.GPT2 model using a particular length bin on DUC2002. Set --desired_target_number to the desired length bin - 1. E.g., if the desired length bin is 1, you should set --desired_target_number 0.

python3 -u gpt2_summarization_prediction.py \
--model_type gpt2 \
--model_name_or_path saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_1_512/epoch_states/18-epoch \
--tokenizer_name saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_1_512 \
--pred_path pred/predict_rl_distilgpt2_duc_512_control_mode_1_bin_1 \
--data_dir ../../datasets/duc2002_preprocessed_long \
--split test \
--temperature 1.0 \
--batch_size 8 \
--beam_size 5 \
--control_modes 1 \
--desired_target_number 0 \
--multiple_reference \
--seed 9527 \
--input_trunc_length 512

Compute ROUGE scores for bin 1, bin4, bin 7, and bin 10 respectively.

python evaluate_prediction.py -rouge -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_1 -data ../../datasets/duc2002_preprocessed_long -multi_ref -n_words 33
python evaluate_prediction.py -rouge -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_4 -data ../../datasets/duc2002_preprocessed_long -multi_ref -n_words 46
python evaluate_prediction.py -rouge -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_bin_7 -data ../../datasets/duc2002_preprocessed_long -multi_ref -n_words 59
python evaluate_prediction.py -rouge -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_bin_10 -data ../../datasets/duc2002_preprocessed_long -multi_ref -n_words 94

Compute bin % for bin 1, bin4, bin 7, and bin 10 respectively.

python output_len_stat.py -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_1 -data_dir ../../datasets/duc2002_preprocessed_long -split test -target_len_bin 9 -multi_ref
python output_len_stat.py -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_4 -data_dir ../../datasets/duc2002_preprocessed_long -split test -target_len_bin 6 -multi_ref
python output_len_stat.py -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_7 -data_dir ../../datasets/duc2002_preprocessed_long -split test -target_len_bin 3 -multi_ref
python output_len_stat.py -decode_dir pred/predict_rl_distilgpt2_duc_512_control_mode_1_epoch_18_bin_10 -data_dir ../../datasets/duc2002_preprocessed_long -split test -target_len_bin 0 -multi_ref

Entity control

Preprocessing

Download QA model here for QA-F1 score. Then put this tar.gz file in the folder ./saved_models and decompress it. If you want to put the QA model in another path, please change the self.model_path and self.tokenizer_path in line 17 and 18 of utils/cloze_model.py.
(Optional) Alternatively, you can also train a QA model by yourself and pick the best checkpoint.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 train_cloze_model.py \
--data_dir ../../datasets/cased-cnn-dailymail_coref_3/cloze_entity_squad_with_idx_and_unanswerable_and_paraphrase_and_repeat \
--model_type bert \
--model_name_or_path twmkn9/bert-base-uncased-squad2 \
--output_dir saved_models/entity_cloze_with_unanswerable_paraphrase_repeat_bert_base_cnn_epoch_16 \
--cache_dir /mnt/sharedfolder/hpchan \
--max_seq_length 500 \
--do_train \
--do_eval \
--evaluate_during_training \
--do_lower_case \
--per_gpu_train_batch_size 11 \
--per_gpu_eval_batch_size 24 \
--gradient_accumulation_steps 1 \
--num_train_epochs 16 \
--logging_steps 8000 \
--save_steps 8000 \
--eval_all_checkpoints \
--fp16

Training

ML training for D.GPT2 model.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_finetuning.py \
--data_dir ../../datasets/cased-cnn-dailymail_coref_3 \
--output_dir saved_models/train_ml_distilgpt2_cnn_control_mode_7_batch_10_4gpu_fp_16_epoch12_entity_first_512_no_datetime \
--model_type gpt2 \
--model_name_or_path distilgpt2 \
--tokenizer_name distilgpt2 \
--cache_dir /data/model_cache \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 6 \
--per_gpu_eval_batch_size 16 \
--learning_rate 5e-5 \
--logging_steps 5000 \
--seed 9527 \
--num_train_epochs 12 \
--max_epochs 12 \
--control_modes 7 \
--gradient_accumulation_steps 3 \
--fp16

RL training for D.GPT2 model.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_rl_finetuning.py \
--data_dir ../../datasets/cased-cnn-dailymail_coref_3 \
--output_dir saved_models/train_distilgpt2_rl_cnn_512_control_mode_7_bertscore_reward_QAF1_1.0_entity_repeat_epoch20 \
--model_type gpt2 \
--model_name_or_path saved_models/train_ml_distilgpt2_cnn_control_mode_7_batch_10_4gpu_fp_16_epoch12_entity_first_512_no_datetime_from_10/epoch_states/12-epoch \
--tokenizer_name saved_models/train_ml_distilgpt2_cnn_control_mode_7_batch_10_4gpu_fp_16_epoch12_entity_first_512_no_datetime_from_10 \
--cache_dir /mnt/sharedfolder/hpchan \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 12 \
--per_gpu_eval_batch_size 24 \
--learning_rate 1.77e-5 \
--max_output_length 75 \
--seed 9527 \
--num_train_epochs 20 \
--reward_type 9 \
--baseline self \
--constrained_mdp \
--cost_types 28 29 15 \
--cost_thresholds 0.0 -0.9 0.0 \
--lagrangian_init_val 0.01 \
--logging_steps 1000 \
--control_modes 7 \
--gradient_accumulation_steps 3 \
--fp16 \
--num_freeze_layers 4

Testing

Download and export the path of ROUGE following the testing procedure of length control.

Reference entity control

Make evaluation reference for dataset. python make_eval_reference.py -data ../../datasets/cased-cnn-dailymail_coref_3 -split test
Decode from D.GPT2 model using reference entities as input. Specify the model_name_or_path to the best checkpoint (highest validation reward).

python3 -u gpt2_summarization_prediction.py \
--model_type gpt2 \
--model_name_or_path saved_models/train_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_QAF1_0.9_epoch20_from_7/epoch_states/20-epoch \
--tokenizer_name saved_models/train_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_QAF1_0.9_epoch20_from_7 \
--pred_path pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_cloze_squad_cost_with_negative_0.9 \
--data_dir ../../datasets/cased-cnn-dailymail_coref_3 \
--split test \
--temperature 1.0 \
--batch_size 8 \
--beam_size 5 \
--control_modes 7 \
--with_ground_truth_input \
--seed 9527 \
--input_trunc_length 512

Compute ROUGE Scores python evaluate_prediction.py -rouge -decode_dir pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_cloze_squad_cost_with_negative_0.9 -data ../../datasets/cased-cnn-dailymail_coref_3
Compute Appear % python evaluate_entity_appear.py --decode_dir pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_cloze_squad_cost_with_negative_0.9 --data ../../datasets/cased-cnn-dailymail_coref_3
Compute QA-F1 python evaluate_cloze.py --decode_dir pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_cloze_squad_cost_with_negative_0.9 --data ../../datasets/cased-cnn-dailymail_coref_3

Entities at different document sentences

Decode from D.GPT2 model for entities at particular document sentences. The option --split test_1to2_sent_ent indicates that we are summarizing the entities in sentences 1 and 2. Alternatively, you can set --split to test_3to4_sent_ent, test_5to6_sent_ent, or test_7to8_sent_ent.

python3 -u gpt2_summarization_prediction.py \
--model_type gpt2 \
--model_name_or_path saved_models/train_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_QAF1_0.9_entity_repeat_epoch20_from_7/epoch_states/9-epoch \
--tokenizer_name saved_models/train_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_QAF1_0.9_entity_repeat_epoch20_from_7 \
--pred_path pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_QAF1_0.9_with_repeat_epoch9_sent_12 \
--data_dir ../../datasets/cased-cnn-dailymail_coref_3 \
--split test \
--temperature 1.0 \
--batch_size 8 \
--beam_size 5 \
--control_modes 7 \
--with_ground_truth_input \
--split test_1to2_sent_ent \
--seed 9527 \
--input_trunc_length 512

Compute Appear % python evaluate_entity_appear.py --decode_dir pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_QAF1_0.9_with_repeat_epoch9_sent_12 --data ../../datasets/cased-cnn-dailymail_coref_3
Compute QA'-F1 python evaluate_cloze.py --decode_dir pred/predict_distilgpt2_rl_cnn_512_rouge2_control_mode_7_bertscore_reward_QAF1_0.9_with_repeat_epoch9_sent_12 --data ../../datasets/cased-cnn-dailymail_coref_3

Abstractivenes Control

Training

ML training for D.GPT2 model on Newsroom-b, change --data_dir to the path of cased-cnn-dailymail if you want to use CNN/DM.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_finetuning.py \
--data_dir ../../datasets/newsroom_guardian_ws_nyt \
--output_dir saved_models/train_ml_distilgpt2_cnn_mode_5_3_bins_batch4_6gpu_fp_16_epoch_10_512_tokens \
--model_type gpt2 \
--model_name_or_path distilgpt2 \
--tokenizer_name distilgpt2 \
--cache_dir /data/model_cache \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 6 \
--per_gpu_eval_batch_size 16 \
--learning_rate 5e-5 \
--logging_steps 5000 \
--seed 9527 \
--num_train_epochs 10 \
--max_epochs 10 \
--control_modes 5 \
--gradient_accumulation_steps 3 \
--input_trunc_length 512 \
--fp16

CMDP training for D.GPT2 model on Newsroom-b, change --data_dir to the path of cased-cnn-dailymail if you want to use CNN/DM.

python3 -m torch.distributed.launch \
--master_port=1234 \
--nproc_per_node 4 gpt2_summarization_rl_finetuning.py \
--data_dir ../../datasets/newsroom_guardian_ws_nyt \
--output_dir saved_models/train_rl_distilgpt2_cnn_mode_5_3_bins_batch4_6gpu_fp_16_epoch_20_512_tokens \
--model_type gpt2 \
--model_name_or_path saved_models/train_ml_distilgpt2_cnn_mode_5_3_bins_batch4_6gpu_fp_16_epoch_10_512_tokens/epoch_states/10-epoch \
--tokenizer_name saved_models/train_ml_distilgpt2_cnn_mode_5_3_bins_batch4_6gpu_fp_16_epoch_10_512_tokens \
--cache_dir /data/model_cache \
--do_train \
--evaluate_during_training \
--per_gpu_train_batch_size 12 \
--per_gpu_eval_batch_size 24 \
--learning_rate 1.77e-5 \
--max_output_length 75 \
--seed 9527 \
--num_train_epochs 20 \
--reward_type 9 \
--baseline self \
--constrained_mdp \
--cost_types 22 15 30 \
--cost_thresholds 0.0 0.0 \
--lagrangian_init_val 0.01 \
--logging_steps 1000 \
--control_modes 5 \
--gradient_accumulation_steps 3 \
--fp16 \
--num_freeze_layers 4

Testing

Decode from D.GPT2 model with abstractiveness bin 1. You should set --desired_target_number 1 for abstractiveness bin 2 and --desired_target_number 2 for abstractiveness bin 3.

python3 -u gpt2_summarization_prediction.py \
--model_type gpt2 \
--model_name_or_path saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_5_new_epoch20/epoch_states/17-epoch \
--tokenizer_name saved_models/train_rl_distilgpt2_cnn_bertscore_control_mode_5_new_epoch20 \
--pred_path pred/predict_rl_distilgpt2_cnn_mode_5_new_3_bins_512_tokens_bin_0 \
--data_dir ../../datasets/newsroom_guardian_ws_nyt \
--split test \
--temperature 1.0 \
--batch_size 4 \
--beam_size 5 \
--control_modes 5 \
--desired_target_number 0 \
--seed 9527 \
--input_trunc_length 512

Evaluate BERTScore

python evaluate_bert_score.py --decode_dir pred/predict_rl_distilgpt2_cnn_mode_5_new_3_bins_512_tokens_bin_0 --data ../../datasets/newsroom_guardian_ws_nyt --model bert-base-uncased --batch_size 40 --rescale-with-baseline --lang en

Evaluate MoverScore

python evaluate_mover_score.py --decode_dir pred/predict_rl_distilgpt2_cnn_mode_5_new_3_bins_512_tokens_bin_0 --data ../../datasets/newsroom_guardian_ws_nyt --batch_size 64

Evaluate Bin % for bin 1 output, you should set -target_abs_bin 1 for abstractiveness bin 2 and -target_abs_bin 2 for abstractiveness bin 3.

python output_extractive_fragment_density_stat.py -data_dir ../../datasets/newsroom_guardian_ws_nyt -decode_dir pred/predict_rl_distilgpt2_newsroom_penalty_mode_5_new_3_bins_512_tokens_bin_0_epoch12_dgx -split test -target_abs_bin 0

Model Output

You can download our decoded summaries here

Values of learned Lagrangian multipliers

The values of learned Lagrangian multipliers λ changes dynamically during training. In the following tables, we report the learned values of λ of our D.GPT2+CMDP model when the validation reward converges.

Length control:

	length bin constraint	3-gram repetition constraint
Values of learned λ	0.3312	0.3333

Entity control:

	QA constraint	entity repetition constraint	3-gram repetition constraint
Values of learned λ	0.1980	0.1810	0.1972

Abstractiveness control:

	Abstractiveness bin constraint	conjunction constraint	3-gram repetition constraint
Values of learned λ (CNN/DM)	0.2842	0.1271	0.2952
Values of learned λ (Newsroom-b)	0.4832	0.2210	0.4898

Distribution of length bins in the CNN/DM training set

Bin	Range	% of samples
1	(0,33]	10.32
2	(33,38]	9.07
3	(38,42]	9.85
4	(42,46]	8.61
5	(46,50]	8.68
6	(50,54]	9.41
7	(54,59]	10.58
8	(59,64]	8.23
9	(64,72]	8.85
10	(72,94]	15.78

kenchan0226 / control-sum-cmdp Goto Github PK

control-sum-cmdp's Introduction

Controllable Summarization with Constrained Markov Decision Process

Dependencies

Datasets

Length Control

Training

Testing

Reference length bin control

Arbitrary length bin control

Entity control

Preprocessing

Training

Testing

Reference entity control

Entities at different document sentences

Abstractivenes Control

Training

Testing

Model Output

Values of learned Lagrangian multipliers

Distribution of length bins in the CNN/DM training set

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