`Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Automatic Science Journalism
Ronald Cardenas, Bingsheng Yao, Dakuo Wang, Yufang Hou
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.
BibTeX
@inproceedings{
cardenas2023dont,
title={`Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Automatic Science Journalism},
author={Ronald Cardenas and Bingsheng Yao and Dakuo Wang and Yufang Hou},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=uZp3i8yEs4}
}