This repository contains the code for AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation.

@inproceedings{iso2024autotemplate,
    title = "AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation",
    author = {Iso, Hayate},
    booktitle = "INLG",
    year = "2024",
    url = "https://arxiv.org/abs/2211.08387",
}

• You can install most of the dependencies with the following commands.

pip install -r requirements.txt

• To evaluate the keywords-to-sentence generation models with METEOR v1.5 score, you can follow the following link to install: https://www.cs.cmu.edu/~alavie/METEOR/README.html
• To evaluate the entity-guided summarization with ROUGE scores, we used the files2rouge: https://github.com/pltrdy/files2rouge

• You can download the raw datasets in the following link: https://github.com/NLPCode/CBART
• Then, you can decompress these datasets and place them under data.

• Run the following to automatically create the train/dev/test splits from raw datasets.

python scripts/prep_k2s.py

• You can train the automatic template generation model with the following commands.
• You can also specify the initial pre-trained checkpoint by changing the model_name parameter.

python train.py ./data/one-billion-words ./log/one-billion-words --model_name google/t5-v1_1-large
python train.py ./data/yelp_review ./log/yelp_review --model_name google/t5-v1_1-large

• You can generate sentences from the set of keywords.
• If you want to generate the sentences from the different number of keywords, you need to change the model input.

python generate.py \
 ./data/one-billion-words/1keywords.jsonl \  # Input path
 ./log/one-billion-words/checkpoint-100000 \  # Model checkpoint
 ./log/one-billion-words/checkpoint-100000/1keywords.txt  # Output path

• Finally, you can evaluate the generated sentences with the following command.
• Note that you need to store generated files with 1-6 keywords to perform evaluation.

python scripts/evaluate_k2s.py \
 ./data/one-billion-words/ \  # directory of reference files
./log/one-billion-words/checkpoint-100000/  # directory of hypothesis files

• We used two major summarization datasets, CNNDM and XSUM.
• You can follow the preprocessing decision made by the BART model refer to this README document: https://github.com/facebookresearch/fairseq/blob/main/examples/bart/README.summarization.md

Run the following to automatically create the train/dev/test splits of the entity-guided summarization datasets.

python scripts/prep_sum.py data/cnndm
python scripts/prep_sum.py data/xsum

• You can train the automatic template generation model with the following commands.
• You can also specify the initial pre-trained checkpoint by changing the model_name parameter.

python train.py ./data/cnndm ./log/cnndm --model_name google/t5-v1_1-large
python train.py ./data/xsum ./log/xsum --model_name google/t5-v1_1-large

• If you want to generate the summaries of the XSum, you need to change the model input and the checkpoint.

python generate.py \
 ./data/cnndm/test.jsonl \  # Input path
 ./log/cnndm/checkpoint-100000 \  # Model checkpoint
 ./log/cnndm/checkpoint-100000/test.hypo  # Output path

• Finally, you can evaluate the generated summaries using files2rouge

files2rouge ./data/cnndm/test.target ./log/cnndm/checkpoint-100000/test.hypo

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