@inproceedings{cardenas-etal-2023-dont,
    title = "{\textquoteleft}Don`t Get Too Technical with Me': A Discourse Structure-Based Framework for Automatic Science Journalism",
    author = "Cardenas, Ronald  and
      Yao, Bingsheng  and
      Wang, Dakuo  and
      Hou, Yufang",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.76/",
    doi = "10.18653/v1/2023.emnlp-main.76",
    pages = "1186--1202",
    abstract = "Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience. We aim to design an automated system to support this real-world task (i.e., automatic science journalism ) by 1) introducing a newly-constructed and real-world dataset (SciTechNews), with tuples of a publicly-available scientific paper, its corresponding news article, and an expert-written short summary snippet; 2) proposing a novel technical framework that integrates a paper`s discourse structure with its metadata to guide generation; and, 3) demonstrating with extensive automatic and human experiments that our model outperforms other baseline methods (e.g. Alpaca and ChatGPT) in elaborating a content plan meaningful for the target audience, simplify the information selected, and produce a coherent final report in a layman`s style."
}