## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling
```
1:functionLengthBasedRefinement(\(S,list\_of\_TA\_repr\_w,list\_of\_RA\_repr\_w\))
2:foreach\(TAtuple\)in\(S\)do
3:if\(TAtuple\_label\
eq^{\prime}SS^{\prime}\)then
4:if\(len(TAtuple.RASent)\geq 2*len(TAtuple.TAsent)\)then
5:\(TA\_sent\_repr\_w\gets Get\_Sent\_W(TAtuple.TAsent,list\_of\_TA\_repr\_w)\)
6:\(RA\_sent\_repr\_w\gets Get\_Sent\_W(TAtuple.RAsent,list\_of\_RA\_repr\_w)\)
7:\(list\_of\_shared\_w\gets Get\_Shared\_W(TA\_sent\_repr\_w,RA\_sent\_repr\_w)\)
8:endif
9:\(list\_ref\_frag\leftarrow[]\)
10:foreachshared\(w\)in\(list\_of\_shared\_w\)do
11:\(list\_ref\_frag.append(left\_frag(TAtuple.RAsent,shared\_w))\)
12:\(list\_ref\_frag.append(right\_frag(TAtuple.RAsent,shared\_w))\)
13:endfor
14:endif
15:if\(Max\_Cosine\_Similarity(TAtuple.TAsent,list\_reference\_frag)\geq HS\)then
16:\(TAtuple.label\leftarrow^{\prime}SS^{\prime}\)
17:endif
18:endfor
19:return\(S\)
20:endfunction
```
**Algorithm 3** The pseudocode of the LengthBasedRefinement() function

The system considers the most representative words of the reference sentence shared with the target one. They consist of the keywords extracted using KeyBERT and named entities. Then, the system uses the left and right contexts for each keyword and named entity in the reference sentence to obtain a set of text fragments. \begin{table}
\begin{tabular}{l l} \hline \hline
**Source** & **Sentence** \\ \hline Fox News (target) & _’The FBI also has been called to investigate the incident.’_ \\ \hline \multirow{3}{*}{CNN International (reference)} & _’The FBI is investigating the incident_, which drew \\  & widespread condemnation of the officers after a video \\  & showing part of the encounter circulated on social media.’ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of sentences that share pieces of core information but with a low cosine score, equal to 0.58. Each text fragment is compared with the target sentence. If the two sentences share some content information, then at least one (_target sentence, text fragment_) pair will obtain a cosine greater than or equal to HS. If this occurs, the system changes the labeling from VDS or DS to SS for the target sentence; otherwise, no change is made. The iteration continues until there are no more pairs in the VDS or DS groups to compare. As an example, Table 3 shows a fragment obtained from the reference sentence of CNN International in Table 2. This fragment is compared to the target sentence of Fox News, with a cosine score of 0.8. #### 3.2.3 Keyword-Based Refinement

Another aspect we have to take into account is the fact that the Sentence-BERT representations often position sentences that share numerous significant words (e.g., named entities, keywords) closer together in the latent space, thus hindering the descriptive power of the cosine metric in these cases. Table 4 provides an example. Here, sentences share a set of named entities ([PERSON], Pfizer, third dose), and this yields a high cosine similarity. However, their informative content (and arguably, meaning) is rather different. To deal with this limitation, we further re-examine all the target sentences included in the SS and DS groups. For each of these sentences, the system checks if the information contained in the target sentence is similar to the one included in the reference sentence, excluding all the common keywords and named entities, by adopting the following method, which is based on the function _KeywordBasedRefinement()_ described in the pseudo-code shown in Algorithm 4. For each pair \(s_{j}^{t_{i}}\) in \(t_{i}\) and \(s_{l}^{r}\) in \(r\), first, the common keywords and named entities are identified. Then the system generates text fragments from the two sentences, excluding those keywords and named entities. Finally, the system compares each text fragment from the reference article with all the text fragments from the target article. If all the combinations of text fragments extracted have a cosine score lower than LS, the class of the target sentence is changed to VDS.