@inproceedings{cardenas-etal-2023-dont,
title = "{`}Don{'}t Get Too Technical with Me{'}: A Discourse Structure-Based Framework for Automatic Science Journalism",
author = "Cardenas, Ronald and
Yao, Bingsheng and
Wang, Dakuo and
Hou, Yufang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.76",
doi = "10.18653/v1/2023.emnlp-main.76",
pages = "1186--1202",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T ‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism
%A Cardenas, Ronald
%A Yao, Bingsheng
%A Wang, Dakuo
%A Hou, Yufang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cardenas-etal-2023-dont
%X 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.
%R 10.18653/v1/2023.emnlp-main.76
%U https://aclanthology.org/2023.emnlp-main.76
%U https://doi.org/10.18653/v1/2023.emnlp-main.76
%P 1186-1202
Markdown (Informal)
[‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism](https://aclanthology.org/2023.emnlp-main.76) (Cardenas et al., EMNLP 2023)
ACL