\section{Related Work}\label{survey}

%%%%%%%%%%%% abstractive methods %%%%%%%%%%%%%%%
% Researchers have developed many statistical methods for automatic summarization and there are basically two main types. With wide popularity of neural network in NLP, many attention based abstractive methods~\cite{rush2015neural,chopra2016abstractive,nallapati2016abstractive} are applied in large-sized corpora.
% Aimed to improve the quality of generated summary, recent methods consider coverage~\cite{DBLP:journals/corr/SuzukiN17,chen2016distraction,see2017get}, copy mechanism~\cite{miao2016language,see2017get}, RL based metric optimization~\cite{paulus2017deep} and alignment relationship~\cite{lin2018global}.

% Compared with above approaches,  methods can be applied in more realistic situations as they are faster, simpler, more reliable on grammar and semantic information.
\textbf{Extractive Text Summarization} Researchers have developed many statistical methods for automatic extractive summarization.
Traditional methods learn to score each sentence dependently~\cite{erkan2004lexrank,mihalcea2004textrank,wong2008extractive}.
Recently neural network based extractive methods~\cite{cheng2016neural,nallapati2017summarunner,feng2018attentive,shi2018deepchannel} usually consider extractive summarization as sequence labeling tasks and aim to minimize the cross-entropy objective function. \citet{narayan2017neural} utilizes side information to help sentence classifier while \citet{yasunaga2017graph} computes the salience of each sentence for selection with graph convolutional networks. In addition, reinforcement learning based methods~\cite{wu2018learning,narayan2018ranking,yao2018deep} have been proposed to directly optimize the evaluation metric ROUGE by combining cross-entropy loss with rewards from policy gradient reinforcement learning.
\citet{dong2018banditsum} considered extractive summarization as a contextual bandit and it performs well especially when good summary sentences appear late in the source document.
Recently, 
\citet{nallapati2017summarunner,chen2018fast,hsu2018unified} propose unified models and combine the advantages of both extractive and abstractive methods.

\begin{table}[t]\scriptsize \small
\centering
\begin{tabular}{|l|c|c|c|}
\hline
Model           & Overall       & Coverage      & \multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Non-\\ Redundancy\end{tabular}} \\ \hline
HER w/o $\mbox{Dec}_2$ & 2.88          & 2.81          & 2.81                                                                          \\ \hline
HER w/o L   & 3.02          & 2.96          & 2.75                                                                           \\ \hline
BANDITSUM       & 2.06          & 2.07 & \textbf{1.91}                                                                           \\ \hline
HER             & \textbf{1.81} & \textbf{1.75}          & 1.97                                                                \\ \hline
\end{tabular}
\caption{Average rank of human evaluation in terms of overall performance, coverage, and non-redundancy. L is short for local Net. Lower score is better.}%\vspace{-3mm}
\label{human_eval}
\end{table}

\textbf{Human Reading-inspired Strategy in NLP}
Recently, several pioneer researches began to study how to adapt human reading cognition process, usually including pre-reading, reading and post-reading~\cite{avery1997scaffolding,saricoban2002reading,toprak2009three,pressley2012verbal}, into various NLP-related applications. For example, \citet{li2018unified} solved document-based question answering and by simulating human being's reading strategy.
\citet{luo2018beyond,luo2019unsupervised} utilized the prior knowledge of human reading to solve sub-tasks in sentiment analysis.
\citet{song-etal-2017-learning,ijcai2018-0608} enhanced word embeddings in a similar way.
\citet{yang2019exploring} applied it for abstractive summarization, \citet{Zheng2019Human} simulated human behavior for reading comprehension, and \citet{Lei2019Human} utilized human-like semantic cognition for aspect-level sentiment classification. In this paper, we attempt to perform extractive summarization under the inspiration of human reading recognition.

















