## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling
```
1:functionTargetSentenceReduction(\(S\))
2:\(list\_DS\_VDS\leftarrow[]\)
3:for\(TAtuple\)in\(S\)do
4:if\(TAtuple.label\
eq^{\prime}SS^{\prime}\)then
5:\(list\_DS\_VDS.append(TAtuple)\)
6:endif
7:endfor
8:\(list\_DS\_VDS\_ranked\gets Ranking(list\_DS\_VDS)\)//(Each sentence in the target article is ranked based on the similarity value with sentences in the reference article)
9:\(list\_of\_filtered\_sentences=[]\)
10:for\(TAtuple\)in list_DS_VDS_rankeddo
11:if\(Max\_Cosine\_Sim(TAtuple,NextSentsInList(TA\_tuple,list\_DS\_VDS\_ranked))\)\(<HS\)then
12:\(list\_of\_filtered\_sentences.append(TAtuple)\)
13:endif
14:endfor
15:return\(list\_of\_filtered\_sentences\)
16:endfunction
```

**Algorithm 5** The pseudocode of the TargetSentenceReduction() function

First, the system takes the set of labeled target articles \(T_{C}=\{t_{1},t_{2},\ldots,t_{n}\}\) as input, and merges the sentences of target articles that are labeled as DS and VDS. SS is disregarded as it can be considered redundant with the reference article. We rank the list of DS plus VDS sentences with respect to their maximum similarity with sentences in \(r\) in descending order. Then, starting from the top, for each sentence, we compute its similarity score with all the other sentences on the list. If at least one score is higher than the HS threshold, i.e., there is another sentence similar to the one considered that also has a lower similarity score with the reference article, we consider the sentence redundant and remove it from the list. The final list includes only sentences that are considered non-redundant. ### Target Sentence Summarization

The goal of the system is to supply users with information they may have missed with the article of their choice (i.e., the reference article) in an easy-to-read format. With the aim of making the information promptly available to the user, the system generates a summary of the target sentences and outputs this summary. According to our definition, all the sentences labeled VDS are selected as they certainly correspond to sentences with information different from the reference article. In addition, all the sentences classified DS with a cosine similarity score lower than LS; all the sentences in VDS are selected as well. All the sentences selected are merged into a unique text. The system then creates a summary of this text by exploiting a neural text summarization model. In particular, we use Google PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization) [39]. It is a pre-trained large Transformer-based encoder-decoder model. PEGASUS uses a gap-sentence generation strategy, in which important sentences are masked by an input document and are then generated together as one output sequence from the remaining sentences. The final output is a unique and readable text that only reports information included in VDS sentences and some of the DS sentences for each target article. In this way, we can visualize the new content in the target articles with respect to the reference one. In Table 5, an example derived from article segments on the Notre Dame fire is presented. The reference article is part of an article published on 16 April 2019 on NBC News, while the two target articles are extracted from the set of articles published on the same date by USA Today and Al Jazeera English. These three example articles are included in a dataset of international newspapers (e.g., CNN, The Guardian, Al Jazeera English) about events that happened occurred 2019 and 2021. The dataset is available on GitHub [[https://github.com/pietrodelloglio/dataset-for-system-acquiring-content-differences](https://github.com/pietrodelloglio/dataset-for-system-acquiring-content-differences)]([https://github.com/pietrodelloglio/dataset-for-system-acquiring-content-differences](https://github.com/pietrodelloglio/dataset-for-system-acquiring-content-differences)) (accessed on 3 November 2023).