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Decision-Focused Summarization

Impletmentation of our EMNLP2021 paper, Decision-Focused Summarization paper link.

Env

Create env with conda:

conda create -n yelp python=3.7.6

Then install packages with:

cat requirements.txt | sed -e '/^\s*#.*$/d' -e '/^\s*$/d' | xargs -n 1 python -m pip install
# download spacy package
python -m spacy download en_core_web_sm

# If you are using RTX3090, try the following step to install pytorch
pip install torch==1.7.0+cu110 -f https://download.pytorch.org/whl/torch_stable.html

Data Preprocessing

Here, we only select reviews from restaurants to build our dataset.

Download raw dataset from https://www.yelp.com/dataset/download and uncompress it to YELP_DATA_DIR. Then, run scrip at the base directory.

python -m preprocess.yelp_preprocess [--yelp_data_dir YELP_DATA_DIR] [--output_dir OUTPUT_DIR]

Train Longformer model

Remeber to change env variables in scripts/train_transformer.sh before running the training script. It takes about three hours to train longformer on RTX3090 with half precision.

bash scripts/train_transformer.sh

You can check training log here ${OUTPUT_DIR}/logs/ with tensorboard. Trained model will be saved to path like this ${OUTPUT_DIR}/version_27-12-2021--16-59-15/checkpoints/epoch=1-val_loss=0.12.ckpt.

Run DecSum

Change env variables in scripts/sentence_select.sh before running DecSum. This step takes about 10 hours on RTX3090.

# at base Directory
bash scripts/sentence_selection.sh

The DecSum summaries will be saved at ${RES_DIR}/models/sentence_select/selected_sentence/yelp/50reviews/test/Transformer/window_1_DecSum_WD_sentbert_50trunc_1_1_1/best/1/text_.csv.

MSE with True Label metric will be store at ${RES_DIR}/models/sentence_select/results/yelp/50reviews/test/Transformer/window_1_DecSum_WD_sentbert_50trunc_1_1_1/best/1/text_.csv.

Get Decision Scores for Individual Sentences

Change env variables in scripts/single_sentence_score.sh before running. This step takes about an hour on RTX3090.

# at base Directory
bash scripts/single_sentence_score.sh

Results will be saved at ${RES_DIR}/models/sentence_select/selected_sentence/yelp/50reviews/test/Transformer/window_1/order/10000/text_.csv. Sentences are in the original order for each restaurants (business).

Baseline methods

cleaning

Generating Experiment Plots

cleaning

Citation

@inproceedings{hsu-tan-2021-decision,
    title = "Decision-Focused Summarization",
    author = "Hsu, Chao-Chun  and
      Tan, Chenhao",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.10",
    doi = "10.18653/v1/2021.emnlp-main.10",
    pages = "117--132",
}