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