PaperRobot: Incremental Draft Generation of Scientific Ideas

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1980–1991 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1980 PaperRobot: Incremental Draft Generation of Scientific Ideas Qingyun Wang1, Lifu Huang1, Zhiying Jiang1, Kevin Knight2, Heng Ji1,3, Mohit Bansal4, Yi Luan5 1 Rensselaer Polytechnic Institute 2 DiDi Labs 3 University of Illinois at Urbana-Champaign 4 University of North Carolina at Chapel Hill 5 University of Washington kevinknight@didiglobal.com, hengji@illinois.edu Abstract We present a PaperRobot who performs as an automatic research assistant by (1) conduct- ing 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 con- textual text attention; (3) incrementally writ- ing some key elements of a new paper based on memory-attention networks: from the in- put 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 com- pare 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, respec- tively.1 1 Introduction Our ambitious goal is to speed up scientific dis- covery and production by building a PaperRobot, who addresses three main tasks as follows. Read Existing Papers. Scientists now find it difficult to keep up with the overwhelming amount of papers. For example, in the biomedical domain, on average more than 500K papers are published every year2, and more than 1.2 million new pa- pers are published in 2016 alone, bringing the to- tal number of papers to over 26 million (Van No- orden, 2014). However, human’s reading ability 1The programs, data and resources are publicly avail- able for research purpose at: https://github.com/ EagleW/PaperRobot 2http://dan.corlan.net/medline-trend/ language/absolute.html keeps almost the same across years. In 2012, US scientists estimated that they read, on aver- age, only 264 papers per year (1 out of 5000 available papers), which is, statistically, not dif- ferent from what they reported in an identical sur- vey last conducted in 2005. PaperRobot automat- ically reads existing papers to build background knowledge graphs (KGs), in which nodes are enti- ties/concepts and edges are the relations between these entities (Section 2.2). Abstract Conclusion and Future Work Human Written Title Old Human Written Papers Enriched Knowledge Graphs New Title Abstract ... 1st Paper 2nd Paper Conclusion and Future Work Figure 1: PaperRobot Incremental Writing Create New Ideas. Scientific discovery can be considered as creating new nodes or links in the knowledge graphs. Creating new nodes usu- ally means discovering new entities (e.g., new pro- teins) through a series of real laboratory experi- ments, which is probably too difficult for Paper- Robot. In contrast, creating new edges is eas- ier …

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