## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling  
Function _TargetSentenceLabeling()_ is described in the pseudo-code shown in Algorithm 2. Once each sentence \(s_{j}^{t_{i}}\) in \(t_{i}\) and each sentence \(s_{l}^{\prime}\) in \(r\) are represented as embeddings, we compute the similarity between \(s_{j}^{t_{i}}\) and \(s_{l}^{\prime}\), for each sentence in \(t_{i}\) and \(r\), by exploiting the cosine similarity. In Table 1, we summarize how the sentences \(s_{j}^{t_{i}}\) in \(t_{i}\) are classified based on values of maximum cosine similarity and provide a description for each class. ```
1:functionTargetSentenceLabeling(\(RA\_enc\_sent\_set\_set\_TA\_enc\_sent\_set\_S\))
2:for each\(TA\_enc\_sent\_set\_set\)do
3:if\(Max\_Cosine\_Similarity(TA\_sent\_RA\_enc\_sent\_set\_)\geq HS\)then
4:\(label\gets'SS^{\prime}\)
5:elseif\(Max\_Cosine\_Similarity(TA\_sent\_RA\_enc\_sent\_set)<LS\)then
6:\(label\gets'VDS^{\prime}\)
7:else
8:\(label\gets'DS^{\prime}\)
9:endif
10:\(RAsent\gets Most\_Similar\_to\_Target(RA\_enc\_sent\_set)\)
11:\(TAtuple\leftarrow[TA\_sent\_label\_,RAsent]\)
12:\(S.append(TAtuple)\)
13:endfor
14:return\(S\)
15:endfunction \begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Classification Rules** & **Description** \\ \hline \multirow{3}{*}{SS} & **IF** maximum cosine similarity between \(s_{j}^{t_{i}}\) and & Sentences in this class include pieces of \\  & each sentence in reference article \(r\geq\) HS **THEN** &  information that are shared with the reference \\  & classify \(s_{j}^{t_{i}}\) as SS & article. This information likely refers to the \\ \hline \multirow{3}{*}{DS} & **IF** maximum cosine similarity between \(s_{j}^{t_{i}}\) and & Most of the sentences in this class include \\  & each sentence in reference article \(r<\) HS **AND**\(\geq\) &  pieces of information that are likely shared \\  & LS **THEN** classify \(s_{j}^{t_{i}}\) as DS & with reference sentences but reported \\ \hline \multirow{3}{*}{VDS} & **IF** maximum cosine similarity between \(s_{j}^{t_{i}}\) and & Sentences containing information different \\  & each sentence in reference article \(r<\) LS **THEN** &  from the information in the reference \\ \cline{1-1}  & classify \(s_{j}^{t_{i}}\) as VDS & sentences; it also contains some noise. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the three classes and corresponding classification rules based on the maximum cosine similarity between the sentence \(s_{j}^{t_{i}}\) of the target article \(t_{i}\) under analysis and the sentences of the reference article \(r\). #### 3.2.2 Length-Based Refinement

The annotation step described in the previous subsection suffers from the following problem: when sentences vary significantly in length, their calculated similarity may be low despite them sharing similar content. Indeed, length is an essential factor for BERT-like models. Although sentence-BERT has proved to provide semantically meaningful representations of sentences in the vector space [5], the cosine similarity computed between embeddings of sentences with similar content but different lengths is not generally as high as expected for the scope of the presented system. Table 2 shows an illustrative example. The two sentences express the same information. Nevertheless, since their difference in length is high, the cosine similarity computed between the corresponding embeddings is too small for labeling the target sentence as SS. Thus, the system reiterates all target-reference sentence pairs, specifically focusing on those target sentences not labeled as SS, and that are significantly shorter in terms of tokens compared to the reference sentence (specifically, the reference sentence is at least two times longer in terms of tokens with respect to the target one). The system then determines whether the longer sentence contains additional information by employing the following method, which is based on the function _LengthBasedRefinement()_ described in the pseudo-code shown in Algorithm 3.