\begin{table}[t]
\centering
%\centering
\begin{tabular}{|l|l|l|l|}
\hline
Model        & \multicolumn{3}{l|}{ROUGE}                    \\ \hline
             & R1            & R2            & RL            \\ \hline
HER w/o F      & 40.2          & 17.5          & 36.2          \\
HER w/o Local  & 41.1          & 18.3          & 37.1          \\
HER w/o Global & 40.5          & 18.2          & 36.9          \\ \cdashline{1-4}[1.5pt/2pt]
HER w/o greedy   & 42.1          & \textbf{18.9} & 37.6          \\ \cdashline{1-4}[1.5pt/2pt]
HER-3        & 42.0          & 18.6          & 37.5          \\ \cdashline{1-4}[1.5pt/2pt]
\textbf{HER} & \textbf{42.2} & \textbf{18.9} & \textbf{37.9} \\ \hline
\end{tabular}
\caption{The results of ablation test on the combined CNN/DailyMail test sets.}
\label{Ablation_table}
\end{table}

\subsection{Ablation Test}\label{Ablation}
In this section, we conduct ablation test to assess the effectiveness of our modules.
% importance in rough reading
%1. No local net
%2. No general net
%3. No rough
We remove the whole rough reading process where sentences vectors are directly decoded to affinities, the local features, the global feature individually, and we denote these three models as HER w/o F, HER w/o Localv, HER w/o Global.
As shown in Table~\ref{Ablation_table}, rough reading process is very important in summarization as it extract rough but informative features in advance to help fine-grained extraction process in careful reading. Furthermore, the global feature has significantly influence than local feature. We conjecture it is because the main idea is the key for summarization as sentences whose meaning close to the general idea are presumably included in the summary.
But local features help complete the summary with more thorough information and improve the coverage of original context.

%*****e-greedy****
%1. only extract M sentences
%2. termination
During sentence selection, our model HER use $\epsilon$-greedy to select one sentence at each step~(Sec.\ref{selection}) in order to raise the exploration chances on discovering important but easily ignored information.
We replace above policy with greedy selection which tends to extract the sentence with highest affinity value at each time util terminated and present it as HER w/o greedy. In Table~\ref{Ablation_table}, it shows that HER w/o greedy performs a slightly worse than our proposed model.


%*****termination****
%1. only extract M sentences
%2. termination
To evaluate the effectiveness of our termination mechanism, we only extract three sentences for every document presented as HER-$3$.
It proves that HER constructed with an automatic termination mechanism is more flexible and reliable in extracting various numbers of sentences varying different documents.
% where $k=min(3,|D|)$ and $|D|$ is the document length. 
