  Reading and writing research papers is one of the most privileged abilities
that a qualified researcher should master. However, it is difficult for new
researchers (\eg{students}) to fully {grasp} this ability. It would be
fascinating if we could train an intelligent agent to help people read and
summarize papers, and perhaps even discover and exploit the potential knowledge
clues to write novel papers. Although there have been existing works focusing
on summarizing (\emph{i.e.}, reading) the knowledge in a given text or
generating (\emph{i.e.}, writing) a text based on the given knowledge, the
ability of simultaneously reading and writing is still under development.
Typically, this requires an agent to fully understand the knowledge from the
given text materials and generate correct and fluent novel paragraphs, which is
very challenging in practice. In this paper, we propose a Deep ReAder-Writer
(DRAW) network, which consists of a \textit{Reader} that can extract knowledge
graphs (KGs) from input paragraphs and discover potential knowledge, a
graph-to-text \textit{Writer} that generates a novel paragraph, and a
\textit{Reviewer} that reviews the generated paragraph from three different
aspects. Extensive experiments show that our DRAW network outperforms
considered baselines and several state-of-the-art methods on AGENDA and
M-AGENDA datasets. Our code and supplementary are released at
https://github.com/menggehe/DRAW.
