## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling
was rated on these 3 characteristics by 7 different people on a Likert-type scale from 1 to 7, with 1 and 7 being the lowest and the highest scores, respectively. Figure 11 shows the human judgments for each news event regarding the novelty, i.e., the amount of new information contained in the target summary compared to the reference article. On average, the score is 3.5. Some news items have better evaluations than others. In particular, the summaries on [PERSON] coming out, [PERSON]'s death, and the Mars landing obtain majority scores between 4 and 6, while the news about Italy winning the Euro championship obtains a low score. Figure 12 presents a box plot depicting the human judgments for each news event regarding the factual correctness of the target summary in comparison to the reference article. The average score is 4.6, which is quite high, with the majority of scores for nearly all news items ranging between 5 and 6. Finally, Figure 13 presents human judgments on information completeness for each news event, with an average score of 3.8, which is encouraging, as we observe that the majority of scores are high for certain news events, such as [PERSON] coming out and [PERSON]'s death, while they are slightly lower for others. Table 8.0 Box plot of human judgments for each news event on the amount of new information contained in the summary, with respect to the reference article. Table 8.1 Box plot depicting human judgments on the factual correctness of target summaries for each news eventrelative to the reference article. The experiment shows that the automatically generated summaries are able to provide an adequate overview of the target sentences. Specifically, correctness ratings are, on average, rather satisfactory, indicating that the summaries provide factually correct texts with respect to the original news. Completeness ratings are also quite high, even though a significant drop in quality is observed for some of the considered news. Novelty ratings, while generally lower, are still encouraging. Thus, we can conclude that, although some users are less satisfied than others, depending on the specific news, on average, the system is able to output comprehensible and coherent summaries. ### An Example of the Application

In this section, we show an example of how the proposed system can be applied. We choose a reference news article about the war in Ukraine, titled \"Second victim of New Year's Eve strikes on Kyiv dies in hospital\", published on CNN International on 2 January 2023. The system was run on the same date at 10:21 a.m. (CET). The text and the link to the article are included in Attachment S1 of the supplemental material. The 'Target Articles Retrieval' module collected four different articles, all on the same event but focusing on different aspects. The newspaper websites selected for the search were The New York Times, NBC News, Al Jazeera English, USA Today, and BBC News. The system retrieved articles only from NBC News, Al Jazeera English, and USA Today websites. On that particular day, there was no article similar to the reference article in the New York Times and on BBC News websites. The titles of the target articles are listed in Table 8.2 \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Title** & **Event** & **Source** & **Date** \\ \hline Music and missile strikes: Ukraine & & & \\ rings in the New Year under & Ukraine war & NBC News & 2 January 2023 \\ Russian attack. & & & \\ \hline Air raid sirens in Kyiv as Russia & & & \\ launches fresh drone attacks. & Ukraine war & Al Jazeera English & 2 January 2023 \\ \hline Renewed Russian attacks mark & & & \\ Ukraine’s grim start to 2023. & Ukraine war & Al Jazeera English & 2 January 2023 \\ \hline Kyiv residents ringing in new year & & & \\ caught in Russian attack; [PERSON] & & & \\ says Ukraine will not & Ukraine war & USA Today & 2 January 2023 \\ forgive: Updates. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The metadata of the target articles.