Title
We Can Explain Your Research In Layman'S Terms: Towards Automating Science Journalism At Scale
Abstract
We propose to study Automating Science Journalism (ASJ), the process of producing a layman's terms summary of a research article, as a new benchmark for long neural abstractive summarization and story generation. Automating science journalism is a challenging task as it requires paraphrasing complex scientific concepts to be grasped by the general public. Thus, we create a specialized dataset that contains scientific papers and their Science Daily press releases. We demonstrate numerous sequence to sequence (seq2seq) applications using Science Daily with the aim of facilitating further research on language generation, which requires extreme paraphrasing and coping with long research articles. We further improve the quality of the press releases using co-training with scientific abstracts of sources or partitioned press releases. Finally, we apply evaluation measures beyond ROUGE, and we demonstrate improved performance over strong baselines, which we further confirm by quantitative and qualitative evaluation.
Year
Venue
DocType
2021
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Conference
Volume
ISSN
Citations 
35
2159-5399
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Rumen Dangovski121.39
Michelle Shen200.34
Dawson A Byrd300.34
Li Jing4165.47
Desislava M Tsvetkova500.34
Preslav I. Nakov61771138.66
Marin Soljacic7195.16