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AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation

Conference arXiv

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

Citations

@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",
}

Setup

  • You can install most of the dependencies with the following commands.
pip install -r requirements.txt

Keywords-to-Sentence generation task

1. Download the raw datasets

2. Preprocess the datasets

  • Run the following to automatically create the train/dev/test splits from raw datasets.
python scripts/prep_k2s.py

3. Run the training script

  • 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

4. Generate sentences from the pre-specified keywords

  • 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

5. Evaluate the generated sentences

  • 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

Entity-guided summarization

1. Download the raw datasets

2. Preprocess the datasets

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

3. Run the training script

  • 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

4. Generate summaries based on the source article and constrained entities

  • 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

5. Evaluate the generated summaries

  • Finally, you can evaluate the generated summaries using files2rouge
files2rouge ./data/cnndm/test.target ./log/cnndm/checkpoint-100000/test.hypo

Disclosure

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