PaperRobot: Incremental Draft Generation of Scientific Ideas
Published in The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), 2019
Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan (2019). PaperRobot: Incremental Draft Generation of Scientific Ideas in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. (ACL 2019).
[Paper] [Bib] [Paper Reading Dataset] [Paper Writing Dataset] [Code] [Poster] [Sample Output]
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.