SGG

This repo provides the code for reproducing the experiments in NAACL2021 paper: SGG: Learning to Select, Guide, and Generate for Keyphrase Generation

Figure : Illustrations of SGG framework

Environment

python3
tensorflow-gpu version: 1.13

Train

python run_summarization.py
--mode=True
--coverage=False
--enhanced_attention=True # True=SGG, False=SG
--data_path=dataset/testset_*
--batch_size=64
--vocab_path=dataset/vocab
--log_root=SG
--exp_name=myexperiment
--single_pass=True

Inference

python run_summarization.py
--mode=True
--coverage=False
--enhanced_attention=True # True=SGG, False=SG
--data_path=dataset/testset_*
--vocab_path=dataset/vocab
--log_root=SG
--exp_name=myexperiment
--single_pass=True

Citation

@inproceedings{zhao-etal-2021-sgg,
    title = "{SGG}: Learning to Select, Guide, and Generate for Keyphrase Generation", 
    author = "Zhao Jing and Bao Junwei and Wang Yifan and Wu Youzheng and He Xiaodong and Zhou Bowen", 
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", 
    year = "2021",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.455",
    pages = "5717--5726",
}