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 framework outperforms other baseline methods (e.g. Alpaca and ChatGPT) in elaborating a content plan meaningful for the target audience, simplifying the information selected, and producing a coherent final report in a layman's style.
Cite
CITATION STYLE
Cardenas, R., Yao, B., Wang, D., & Hou, Y. (2023). “Don’t Get Too Technical with Me”: A Discourse Structure-Based Framework for Science Journalism. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1186–1202). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.76
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