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'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism

arXiv 2310.15077 ↗First posted 23 Oct 2023cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 23 Oct 2023.

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21 of 49 references.

References · 21

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9 more
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