\section{Experiment Settings}\label{Experiment}
In this section we present our experimental setup for evaluating the performance of the proposed HER, including the datasets, evaluation protocol, baselines and implementation details.


\textbf{Datasets:} 
We evaluate our models on three datasets: the CNN, the DailyMail and the combined CNN/DailyMail~\cite{hermann2015teaching,nallapati2016abstractive}.
We also use the standard splits of \citet{hermann2015teaching} for training, validation, and test~($90,266/1,220/1,093$ documents for CNN and $196,961/12,148/10,397$ for DailyMail) with the same setting in \citet{see2017get}.

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\textbf{Evaluation:}
We evaluate summarization quality using $F_1$ ROUGE~\cite{lin2004rouge} including unigram and bigram overlap~(ROUGE-1 and ROUGE-2) to assess informativeness and the longest common subsequence~(ROUGE-L) to assess fluency with the reference summaries. We obtain ROUGE scores using a faster python implementation\footnote{\url{https:// github.com/pltrdy/rouge}} for training and evaluation, and the standard pyrouge package\footnote{Pyrouge is a Python package. We compute all ROUGE scores with parameters ``-a -c 95 -m -n 4 -w 1.2.'' Refer to \url{https://pypi.python.org/pypi/pyrouge/ 0.1.3}} for test following~\citet{dong2018banditsum}.


\textbf{Baselines:}
We compare our proposed HER against four kinds of extractive methods:
(1) Lead-$3$ model simply selects the first three sentences.
(2) NN-SE~\cite{cheng2016neural} and SummaRuNNer~\cite{nallapati2017summarunner} are sequence labeling task and trained with cross-entropy loss.
(3) Refresh~\cite{narayan2018ranking}, DQN~\cite{yao2018deep} and RNES~\cite{wu2018learning} extract summary via reinforcement learning.
(4) BANDITSUM~\cite{dong2018banditsum} considers the extractive summarization as a contextual bandit but fails to simulate human reading recognition process.%to extract high-quailty sentences that comes near the end of the document.


\textbf{Implementation Details:}
We initialize word embeddings with $100$-dimension Glove embeddings~\cite{pennington2014glove}.
In rough reading, the encoder is hierarchical and each layer is a two-stacked BiLSTM with a hidden size of $200$. Therefore, sentence vectors and the document representation $\bar S$ have a dimension of $400$. For the variant CNN, we adopt filter windows $H$ in $\{1,2,3\}$ with $100$ feature maps each and generate $K=3$ local representations for each document.
%%%%%%% 解释一下为什么不用pre-trained model for sentence vector
%As the encoder is updated by policy gradient which makes little difference on large pre-trained models,
%above construction is more appropriate to produce features.
In careful reading, we set $\epsilon=0.1$ for bandit policy. We also bound the minimum and maximum number of selected sentence to be $1$ and $10$ for termination mechanism.
%and $\lambda=1.2$ for the first termination mechanism.
During training, we use the optimizer Adam~\cite{kingma2014adam} with a learning rate of $10^{-5}$, beta parameters as~$(0,0.999)$ and a weight decay of $10^{-6}$ to maximize the objective function following \citet{dong2018banditsum}.
We employ gradient clipping of $1$ for regularization and sample $B=20$ times for each document. We train our model within two epochs.
Note that we choose the whole document as the final summary if the document length is less than $3$ sentences as the local features cannot be obtained through the CNN-based network.

% the first $k$ sentences as summary for testing where $k=\mbox{min}(3,|D|)$ and $|D|$ is the document length.

