## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling
At the end of all these steps, the system outputs a set of classified target articles \(T_{C}=\{t_{1},t_{2},\ldots,t_{n}\}\), where \(t_{i}=\left\{s_{1}^{t_{i}},s_{2}^{t_{j}},\ldots,s_{n_{i}}^{t_{i}}\right\}\) is the set of all the sentences of the article \(t_{i}\), labeled as SS, DS, and VDS. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference Sentence** & **Target Sentence** & **Cosine Similarity** \\ \hline \multicolumn{3}{l}{\({}^{\dagger}\)Pfizer anticipates applying for} \\ emergency use authorization for a & \multicolumn{3}{l}{\({}^{\dagger}\)During a company earnings call on} \\
**third dose** of its vaccine as soon as & \multicolumn{3}{l}{Wednesday morning, Dr. **[PERSON]**} \\ next month, **[PERSON]**, who leads worldwide research, & **[PERSON]**, who leads worldwide research, development and medical & 0.7 \\ development and medical for **Pfizer**, & **for Pfizer**, called the new data on a & \\ was quoted by CNN as saying at the & **third dose** of vaccine encouraging.’ & \\ teleconference.’ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Two sentences with a high cosine score that report different information about the same topic but share a set of keywords and named entities (highlighted in bold). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Fragment** & **Target Sentence** & **Cosine Similarity** \\ \hline \multicolumn{3}{l}{\({}^{\dagger}\)The **FBI** is investigating} & \multicolumn{3}{l}{The **FBI** also has been called to} \\ \multicolumn{3}{l}{the incident\({}^{\prime}\)} & \multicolumn{3}{l}{investigate the incident.’} & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An example of a fragment–reference sentence pair with a high cosine similarity score. Shared keyword is highlighted in bold. ```
1:functionKeywordBasedRefinement(\(S\),list_of_TA_repr_w,list_of_RA_repr_wo)
2:foreach\(TAtuple\)in\(S\)do
3:if\(TAtuple\
eq^{\prime}VDS^{\prime}\)then
4:\(TA\_sent\_repr\_w\gets Get\_Sent_W(TAtuple.TAsent,list_of_TA_repr_w)\)
5:\(RA\_sent\_repr\_w\gets Get\_Sent_W(TAtuple.Rasent,list_of_RA_repr_w)\)
6:list_of_shared_w\(\leftarrow\)Get_Shared_W(TA_sent_repr_w,RA_sent_repr_w)
7:list_ref_frag\(\leftarrow\)[]
8:list_target_frag\(\leftarrow\)[]
9:foreachshared_w in list_of_shared_w do
10:list_ref_frag.append(left_frag(TAtuple.Rasent,shared_w))
11:list_ref_frag.append(right_frag(TAtuple.Rasent,shared_w))
12:list_target_frag.append(left_frag(TAtuple.TAsent,shared_w))
13:list_target_frag.append(right_frag(TAtuple.TAsent,shared_w))
14:endfor
15:ifMax_Cosine_Similarity(list_target_frag,list_ref_frag) \(<^{\prime}LS^{\prime}\)then
16:\(TAtuple.label\leftarrow^{\prime}VDS^{\prime}\)
17:endif
18:endif
19:endfor
20:return\(S\)
21:endfunction
```
**Algorithm 4** The pseudocode of the KeywordBasedRefinement() function

### Target Sentence Reduction

Once the labeled set of sentences included in \(T_{C}\) is obtained, we further refine the sentence selection process by determining sentences in \(T_{C}\) that are similar to each other, i.e., redundant. As these sentences generally provide very little further information, we remove them. The target sentence reduction sub-process is based on the function _TargetSentenceReduction()_ described in the pseudo-code shown in Algorithm 5.