molichenxi / haesum

[NAACL 2024 Findings] Source code for our paper"Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level"

Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level

[NAACL 2024 Findings] Source code for our paper "Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level"

paper: https://arxiv.org/abs/2405.10202

Overview

An illustration of modeling an input document from local and global perspectives. Triangles and circles represent words and sentences in the original document respectively.

Setup

Installation

The code is written in Python 3.6+. Its dependencies are summarized in the file requirements.txt. You can install these dependencies like this:

pip install -r requirements.txt

Datasets

Download Pubmed and Arxiv datasets from here.

Preprocess data

For PubMed dataset:

python preprocess_data.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task train
python preprocess_data.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task val
python preprocess_data.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task test

Alternatively, you can directly run preprocesspubmed.sh:

bash preprocesspubmed.sh

For ArXiv dataset:

python preprocess_data.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task train
python preprocess_data.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task val
python preprocess_data.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task test

Alternatively, you can directly run preprocessarxiv.sh:

bash preprocessarxiv.sh

Create Vocabulary & TFIDF Vectors

After getting the standard JSON format, you process the PubMed or Arxiv dataset by running a script:

bash PrepareDataset.sh

The processed files will be put under the cache directory.

Prepare for hyperedges

For PubMed dataset:

python get_hedge.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task train
python get_hedge.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task val
python get_hedge.py --input_path dataset/pubmed-dataset --output_path dataset/pubmed --task test

or

bash get_hedges_pubmed.sh

For ArXiv dataset:

python get_hedge.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task train
python get_hedge.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task val
python get_hedge.py --input_path dataset/arxiv-dataset --output_path dataset/arxiv --task test

or

bash get_hedges_arxiv.sh

Training

Run command like this

python train_hyper.py --cuda --gpu 0 --n_epochs 10 --data_dir <data/dir/of/your/json-format/dataset> --cache_dir <cache/directory/of/graph/features> --model Hypergraph --save_root <model path> --log_root <log path> --lr_descent --grad_clip -m 3

Alternatively, you can directly run the corresponding dataset's .sh file. For example

bash train_arxiv.sh

The training time for one epoch on the PubMed dataset on A6000 48G is 3 hours, while on the Arxiv dataset, it is 6 hours.

Evaluation

For evaluation, the command may like this:

python evaluation.py --cuda --gpu 0 --data_dir <data/dir/of/your/json-format/dataset> --cache_dir <cache/directory/of/graph/features> --embedding_path <glove_path>  --model Hypergraph --save_root <model path> --log_root <log path> -m 5 --test_model multi --use_pyrouge

Note: To use ROUGE evaluation, you need to download the 'ROUGE-1.5.5' package and then use pyrouge.

Error Handling: If you encounter the error message Cannot open exception db file for reading: /path/to/ROUGE-1.5.5/data/WordNet-2.0.exc.db when using pyrouge, the problem can be solved from here. Or you can also refer to the installation tutorial provided by the author in Chinese.

Citation

@article{zhao2024hierarchical,
  title={Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level},
  author={Zhao, Chenlong and Zhou, Xiwen and Xie, Xiaopeng and Zhang, Yong},
  journal={arXiv preprint arXiv:2405.10202},
  year={2024}
}