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'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism (2310.15077v1)

Published 23 Oct 2023 in cs.CL

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.

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Authors (4)
  1. Ronald Cardenas (7 papers)
  2. Bingsheng Yao (49 papers)
  3. Dakuo Wang (87 papers)
  4. Yufang Hou (49 papers)