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\title{Reading Like HER: Human Reading Inspired Extractive Summarization}

% \author{Ling Luo, Xiang Ao, Feiyang Pan, Qing He\\
%   Key Lab of Intelligent Information Processing,\\ Institute of Computing Technology,\\ 
%   Chinese Academy of Sciences, Beijing, China\\
%   University of Chinese Academy of Sciences\\
%   {\tt {luoling18s,aoxiang,panfeiyang,heqing}@ict.ac.cn} \\\And
%   Yan Song \\
%   Tencent AI Lab\\ 
%   {\tt clksong@gmail.com} \\}
\author{
Ling Luo$^{1,3}$,\quad
Xiang Ao$^{1,3}$\thanks{Corresponding author.},\quad
Yan Song$^{2}$,\quad
Feiyang Pan$^{1,3}$,\quad
Min Yang$^{4}$,\quad
Qing He$^{1,3}$\\
\affiliations
$^1$Key Lab of Intelligent Information Processing, Institute of Computing Technology,\\
Chinese Academy of Sciences, Beijing, China\\
$^2$Sinovation Ventures\\
% \quad
$^3$University of Chinese Academy of Sciences\\
$^4$Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences\\
%Waterloo, ON Canada N2L 3G1\\
\emails
\{luoling18s, aoxiang, panfeiyang, heqing\}@ict.ac.cn,
clksong@gmail.com
% min.yang@siat.ac.cn
%dyu@tencent.com
}

\date{}

\begin{document}
\maketitle
\begin{abstract}
%Extractive summarization is the task of producing a shorter version of a document by extracting important sentences from the original context, which preserves authenticity without rewriting the contexts.
%Inspired by human beings' reading cognitive process, we split the process of extractive summarization into two phases: rough reading and careful reading, to simulate how human form summary.
%In rough reading, an encoder is devised to encode a document to sentence vectors and generate some rough information including overall and local information.
%During careful reading, each sentence is estimated with a value and a selection module extract one sentence step by step based on the rough information until a good summary is formed.
In this work, we re-examine the problem of extractive text summarization for long documents.
We observe that the process of extracting summarization of human can be divided into two stages: 1)~a \emph{rough reading} stage to look for sketched information, and 2)~a subsequent \emph{careful reading} stage to select key sentences to form the summary.
By simulating such a two-stage process, we propose a novel approach for extractive summarization. We formulate the problem as a contextual-bandit problem and solve it with policy gradient. We adopt a convolutional neural network to encode gist of paragraphs for rough reading, and a decision making policy with an adapted termination mechanism for careful reading.
Experiments on the CNN and DailyMail datasets show that our proposed method can provide high-quality summaries with varied length, and significantly outperform the state-of-the-art extractive methods in terms of ROUGE metrics.
\end{abstract}

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\section*{Acknowledgements}
This work is partially supported by the National Key Research and Development Program of China under Grant No. 2017YFB1002104, the National Natural Science Foundation of China under Grant No. 91846113, U1811461, 61602438, 61573335, CCF-Tencent RhinoBird Young Faculty Open Research Fund No. RAGR20180111. This work is also funded in part by Ant Financial through the Ant Financial Science Funds for Security Research. Xiang Ao is also supported by Youth Innovation Promotion Association CAS.
This work was also partially supported by SIAT Innovation Program for Excellent Young Researchers (Grant No. Y8G027). Min Yang was sponsored by CCF-Tencent Open Research Fund.

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