Dataset and code for TOIS 2022 paper "Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order"

How to get the dataset

Signed the following copyright announcement with your name and organization. Then complete the form online(https://forms.gle/j7YFeEpCmNvmHgns5) and mail to xiuying.chen#kaust.edu.sa ('#'->'@'), we will send you the corpus by e-mail when approved.

Copyright

The original copyright of all the conversations belongs to the source owner. The copyright of annotation belongs to our group, and they are free to the public. The dataset is only for research purposes. Without permission, it may not be used for any commercial purposes and distributed to others.

Train & Evaluate

First, process the data by data_process/dataset_preprocess.py:

python data_process/dataset_preprocess.py input.json output.json

Then, run the training command:

python run_summarization.py \
		--data_path=/train.json \
		--vocab_path=vocab \
		--eval_path=valid.json\
		--test_path=test.json \
		--lang=en --json_input_key=document \
		--json_target_key=summ --log_root=logs_wiki \
		--exp_name=multi --hidden_dim=256 --max_dec_steps=70 \
		--min_dec_steps=35 --eval_every_step=5000 \
		--dataset_size=140000 --batch_size=16 \
		--kernel_size=20 --optimizer=adam --lr=0.001

Note that, you can use the flag --test_path to specify the dataset to evaluate. By default, we use the validation set val.json to evaluate the model.

After training, the checkpoints can be found at logs_wiki. This code will automatically evaluate the model in every 5000 training steps, and the evaluation results of dataset (specified by --test_path) are listed in file logs_wiki/num_rouge_dict.txt. The frequency of automatic model evaluation can be changed using flag --auto_test_step (default value is 5000 steps).

Citation

@inproceedings{chen2022follow,
  title={Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order},
  author={Xiuying, Chen and Mingzhe, Li and Shen, Gao and Zhangming, Chan and Zhao, Dongyan and Xin, Gao and Xiangliang, Zhang and Yan, Rui},
  booktitle = {Transactions on Information Systems (TOIS '22)},
  publisher = {Association for Computing Machinery},
  year = {2022}
}