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Code for Paper "Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization". This code (ARedSumSentRank) has two versions of adaptive redundancy-aware sentence ranking models: ARedSumCTX and ARedSumSEQ

This code is based on BertSum, pytorch 1.1.0, and python 3.6.9. In order to run this code, you need the following packages.

    pip install multiprocess
    pip install tensorboardX
    pip install pyrouge (first install rouge)
    pip install pytorch_pretrained_bert

Preprocessing Stage 1:


The preprocessing steps for CNN/DailyMail follows BertSum using the non-anonymized version.

Option 1: Download the preprocessed data

Option 2: Preprocess the data yourself.

  1. First, download stories and put all the story files into raw_stories.

  2. Download the StandfordNLP package, unzip it and add this line to your bash profile:

  export CLASSPATH=/path/to/stanford-corenlp-full-2018-10-05/stanford-corenlp-3.9.2.jar 

Replace /path/to/ with the path where the package is actually saved.

  1. Download the urls directory from BertSum and put it in directory ARedSumSentRank. These files are important for dividing data.

New York Times

  1. Get New York Times T19_data

  2. Filter and extract the original data to get stories for NYT50:

    python -input_path /path/to/T19_data -output_path /path/to/working/dir

Preprocessing Stage 2:

Afterwards the preprocessing steps for both datasets are as follows: Go to ARedSumSentRank/src

Step1: Tokenize the stories with StanfordNLP

  python -mode tokenize -raw_path /path/to/raw_stories -save_path /path/to/tokenized/stories

Step2: Extract source and target sentences from the tokenized data save train, validation and test according to the given urls.

  python -mode format_to_lines -raw_path /path/to/cnndm_tokenized_stories -save_path /path/to/cnndm/json_data/ -map_path ../urls/ -lower -n_cpus 4

Step3: Convert the data to Bert format. (filtering, cut long sentence, get labels based on rouge)

  python -mode format_to_bert -raw_path /path/to/cnndm/json_data/cnndm -save_path /path/to/cnndm/bert_data/ -oracle_mode greedy -n_cpus 4 -log_file ../logs/preprocess.log

Note: in each step, -raw_path requires the input path, -save_path requires the path to save the output.

For NYT50, use instead of, and change the path name to the corresponding path of NYT50.



    Train model and validate the model every a few thousand steps, keep the models with top args.save_model_count loss and delete the others to save some space. At last, the final args.save_model_count models will be tested on the test collection. (predicted summaries will be saved) Self-computed Rouge will be output in the log. Perl Rouge will also be output.
    If you want to disable the calculation of the Perl Rouge, add
        cmd_arr.append("-report_rouge False")
    in the code.
    When there are models in the directory, test the model with test set and output the predicted summaries. Output Rouge scores.
    Compute perl version rouge with the predicted summaries. Usually called after the predicted summaries have already been predicted.


"base": BertSum + bilinear matching + listwise loss
"ctx": ARedSumCtx model in the paper
"seq": ARedSumSeq model in the paper


Max number of epochs to train


How many steps to train; whichever stops the training first (max_epoch, train_steps)


Use rouge-based label or not
"t" or "f", "True" or "False", "1" or "0" to indicate


Use rouge score to do validation and select best args.save_model_count models, otherwise use loss to choose models.


"bce": Binary cross entropy loss
"wsoftmax": Weighted softmax (no normalization to label, only softmax normalization to predicted scores)
when rouge based labels are used, loss is always cross entropy between softmax of rouge labels and predicted scores.


Use document embeddings as context or not. currently only applies to seq2seq model.


The probability that the selected sentences are kept as original.
With 1-rand_input_thre probability, selected sentences will be replaced with random sentences in the document.

A Simple Way to Train Models

To run different settings of training and evaluating models, go to src/ run:


The hypter-parameters have been set in the script. You need to set the DATA_ROOT_DIR, DATA_PATH, MODEL_DIR and RESULT_DIR to a valid place. The hyper-parameters and the settings in are the settings corresponding to the paper. The results from re-running models may be a little different from what are reported in the paper. Models and predicted summaries correponding to the reported numbers in the paper can be provided upon request. The models are fine-tuned for train_steps and max_epoch whichever is reached first. Overall, 50000 steps are used as the stop condition in our models.

By default, during training, after each -save_checkpoint_steps the model will be validated on the validation set and at last only models with the top 3 performances (in terms of either loss or ROUGE scores indicated by -valid_by_rouge) will be saved and tested with the test data. Precision and ROUGE scores will be reported by default. Whether to calculate precision and ROUGE can be controlled with -report_precision and -report_rouge respectively.

Note that the multi-GPU training has not been tested. Be carefull if you use the multi-GPU setting. The individual commands to train each model are as follows:

(ARedSum-CTX) Train a Salience Ranker

python -bert_data_path /path/to/cnndm_or_nyt50/bert_data/ -visible_gpus 0 -gpu_ranks 0 -accum_count 2 -report_every 50 -save_checkpoint_steps 2000 -decay_method noam -mode train -model_name base -label_format soft -result_path /path/to/results/cnndm_or_nyt50 -model_path /path/to/model/cnn_or_nyt50

Set -model_path to the place where you want to save the model and set -result_path to the place where the predicted summaries are stored

(ARedSum-CTX) Train a Ranker for Selection

python -fix_scorer -train_from /path/to/the/best/salience/ -bert_data_path /path/to/cnndm_or_nyt50/bert_data/ -visible_gpus 2 -gpu_ranks 0 -accum_count 2 -report_every 50 -save_checkpoint_steps 2000 -decay_method noam -model_name ctx -max_epoch 2 -train_steps 50000 -label_format soft -use_rouge_label t -valid_by_rouge t -rand_input_thre 1.0 -temperature 20 -seg_count 30 -ngram_seg_count 20,20,20 -bilinear_out 20 -result_path /path/to/where/you/want/to/save/the/preidicted/summaries -model_path /path/to/where/you/want/to/save/the/models

For NYT50, change the settings in previous command as follows:

-rand_input_thre 0.8 -temperature 20 -seg_count 30 -ngram_seg_count 10,10,10 -bilinear_out 20

To get the performances without Trigram Blocking, set -block_trigram False. By default, it is set to True.

(ARedSum-SEQ) Train a Sequence Generation Model

python -bert_data_path /path/to/cnndm_or_nyt50/bert_data/ -visible_gpus 2 -gpu_ranks 0 -accum_count 2 -report_every 50 -save_checkpoint_steps 2000 -decay_method noam -model_name seq -max_epoch 2 -train_steps 50000 -label_format soft -use_rouge_label t -valid_by_rouge t -rand_input_thre 0.8 -temperature 20 -result_path /path/to/where/you/want/to/save/the/preidicted/summaries -model_path /path/to/where/you/want/to/save/the/models

Validate or Test the Model

Simple change-mode train to -mode validate or -mode test. When -mode test is used, a specific model name can be fed to -test_from, otherwise, all the models in the directory input to -model_path will be applied on the test data. -mode get_rouge can be used to evaluate the ROUGE scores of the predicted summaries in -result_path.

Run Baselines

Get baseline (Lead 3 or Oracle) performances:

python -mode lead/oracle -bert_data_path /path/to/cnndm_or_nyt50/bert_data/ -result_path /path/to/where/you/want/to/save/the/preidicted/summaries -block_trigram False

-block_trigram True can be applied too if you want to get the performances of lead/oracle + Trigram Blocking


Code for Paper "Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization"






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