PaperRobot: Incremental Draft Generation of Scientific Ideas
Published in The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), 2019
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Abstract
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.
Recommended citation: 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).