
PaperRobot: Incremental Draft Generation of Scientific Ideas - headalgorithm
https://arxiv.org/abs/1905.07870
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freddref
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.

