%0 Generic %1 wang2019paperrobot %A Wang, Qingyun %A Huang, Lifu %A Jiang, Zhiying %A Knight, Kevin %A Ji, Heng %A Bansal, Mohit %A Luan, Yi %D 2019 %K nlp %T PaperRobot: Incremental Draft Generation of Scientific Ideas %U http://arxiv.org/abs/1905.07870 %X 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.

@misc{wang2019paperrobot, 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.}, added-at = {2019-05-22T11:49:36.000+0200}, author = {Wang, Qingyun and Huang, Lifu and Jiang, Zhiying and Knight, Kevin and Ji, Heng and Bansal, Mohit and Luan, Yi}, biburl = {https://www.bibsonomy.org/bibtex/251576fe699c698c1563c048f0c755c0c/straybird321}, description = {[1905.07870] PaperRobot: Incremental Draft Generation of Scientific Ideas}, interhash = {b0a8544bf98a9798fb3d04540f37a698}, intrahash = {51576fe699c698c1563c048f0c755c0c}, keywords = {nlp}, note = {cite arxiv:1905.07870Comment: 12 pages. Accepted by ACL 2019 Code and resource will be available at https://github.com/EagleW/PaperRobot}, timestamp = {2019-05-22T11:49:36.000+0200}, title = {PaperRobot: Incremental Draft Generation of Scientific Ideas}, url = {http://arxiv.org/abs/1905.07870}, year = 2019 }