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.
'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism
A novel framework integrates discourse structure and metadata to automate science journalism, outperforming baselines in producing clear, layman-friendly reports.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 4
- Hosting
- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2310.15077ARXIV-DEFAULT
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- Semantic Scholar