## 3 The Proposed System
### Sentence Classification  
The 'Sentence Classification' module is based on the algorithm described in the pseudo-code shown in Algorithm 1. Upon receiving a reference article and the previously described set of target articles as input, the 'Sentence Classification' module categorizes all sentences within the target articles as SS, DS, or VDS. First, the reference article is split into sentences by using the spaCy pipeline. Furthermore, keywords from the reference article are extracted by using KeyBERT [[https://github.com/[PERSON]/[PERSON]](https://github.com/[PERSON]/KeyBERT)]([https://github.com/[PERSON]/[PERSON]](https://github.com/[PERSON]/KeyBERT)) (accessed on 3 November 2023). These keywords are merged with the named entities of the reference article extracted by the 'Target Articles Retrieval' module to generate a list of representative words for the reference article. KeyBERT leverages Sentence-BERT to obtain word embeddings. Then, it selects keywords under the assumption that their cosine similarity is the highest with respect to the embedding of the whole document. Second, with the aim of obtaining reliable representations of semantically relevant news, we exploit a Sentence-BERT pre-trained model to encode the sentences of the reference article. More specifically, we employ the distiluse-base-multilingual-cased-v2 pre-trained model [42]. It is a multilingual model, and the training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We employed this model to facilitate the generalization of the proposed methodology with newspaper texts in other languages [42]. The sequence of elaborations performed on the reference article is applied to each target article. Once the sentences and their distributional representations with Sentence-BERT are obtained for each target article, the sentences are classified with respect to the similarity with sentences in the reference article by calling the _TargetSentenceLabing()_ function. We formally define our classification problem as follows. Let \(r\) and \(T=\{t_{1},t_{2},\ldots,t_{n}\}\) be, respectively, a reference news article and a set of one or more target articles. Both \(r\) and \(t_{i}\) are represented as sets of sentences, \(r=\left\{s_{1}^{r},s_{2}^{r},\ldots,s_{n}^{r}\right\}\) and \(t_{i}=\left\{s_{1}^{t_{i}},s_{2}^{t_{i}},\ldots,s_{n_{i}}^{t_{i}}\right\}\). The goal is to classify each sentence \(s_{j}^{t_{i}}\) in each \(t_{i}\) with one of the three classes (SS, DS, VDS) based on degrees of similarity with all the sentences in \(r\). In particular, the label is assigned to \(s_{j}^{t_{i}}\) by considering the maximum similarity between \(s_{j}^{t_{i}}\) and all the sentences in \(r\). We employ two thresholds, namely high similarity (HS) and low similarity (LS), to discriminate, respectively, between SS and DS, and DS and VDS. The threshold values are considered as parameters. If at least one sentence in \(r\) has a similarity score higher than HS with \(s_{j}^{t_{i}}\), then \(s_{j}^{t_{i}}\) is classified into the SS class. If no sentence in \(r\) has a similarity score higher than LS with \(s_{j}^{t_{i}}\), then \(s_{j}^{t_{i}}\) is classified into the VDS class. Finally, if none of the two previous conditions are met (i.e., the maximum similarity score between \(s_{j}^{t_{i}}\) and a sentence in \(r\) is between LS and HS thresholds), \(s_{j}^{t_{i}}\) is classified into the DS class. ```
1:\(RA\): the reference article (i.e., the article already read by the user)
2:\(TAs\): the list of target articles (i.e., the articles retrieved by the 'Target Articles Retrieval' module. They refer to the same topic or event as the reference article)
3:\(RA\_named\_entities\): the named entities of the reference article (extracted during the 'Target Articles Retrieval' sub-process)
4:\(TAs\_named\_entities\): the named entities of the target articles (extracted during the 'Target Articles Retrieval' sub-process)
5:\(S\): the list of target sentences labeled by the algorithm.