## 3 The Proposed System
### Sentence Classification
#### 3.2.1 Target Sentence Labeling
Figure 13: Box plot of human judgments for each news event, assessing the information completeness in the summaries of target sentences extracted from the target articles, in comparison to the reference article. The second module of the system takes the target and reference articles as input. Then, the articles are split into sentences, as explained in Section 3. First, the system performs an automated classification of all sentences in the target articles (see Section 3.2). Table 10 shows a subset of target sentences from the Al Jazeera English article titled \"Renewed Russian attacks mark Ukraine's grim start to 2023\", labeled with this approach. As for the target sentence reduction (Section 3.3), for each target article, all the labeled sentences are filtered to obtain only sentences that are not considered redundant. Given the reference article, the system for each target article outputs the list of target sentences that contain new or different information with respect to the reference article. This output is included in Attachment S2 of the supplemental material. Each row in the CSV file \"S2\" consists of the target sentence, the labeling, the reference sentence with the highest similarity to the target one, the similarity score, and an integer that represents the index of the target article assigned by the system. Finally, the output of the target sentence summarization (Section 3.4) is a text summary that reports only novel or different information with respect to the reference article. The sentences included in Attachment S2 are used as input for the abstractive text summarization algorithm. Attachment S3 of the supplemental material includes the results of the target sentence summarization for the example. In particular, we show the summaries of each target article and the summary generated from the sentences of all the target articles. These sentences are those outputted by the target sentence reduction module. An illustrative simple example is shown in Table 5. The interface of the application is shown in Figure 14. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Target Sentence** & **Label** & **Reference Sentence** & **Cosine** \\ \hline Elsewhere, a 22-year-old woman died of wounds from a Saturday rocket attack in the eastern town of Khmelnytskyi, the city’s mayor said. & DS & Another person died and 20 others were injured in Saturday’s explosions, [PERSON] said. & 0.5446 \\ \hline While Russia’s bombardments have left many Ukrainians without heating and electricity due to damage or controlled blackouts meant to preserve the remaining power supply, Ukraine’s. & VDS & A 46-year-old man who was injured by a Russian attack on the Ukrainian capital on Saturday has died in hospital, according to city mayor [PERSON]. & 0.2609 \\ \hline “The power industry is doing everything possible to ensure that the New Year’s holiday is with light, without restrictions”, utility company Ukrenergo said. & “One of the injured as a result of the Russian attack on the capital on December 31 died this morning”, [PERSON] said on his official Telegram channel. & 0.0954 \\ Russia’s RIA state news agency cited a local doctor as saying six people were killed when a hospital in Donetsk was attacked on Saturday. & A 46-year-old man who was injured by a Russian attack on the Ukrainian capital on Saturday has died in hospital, according to city mayor [PERSON]. & 0.5216 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Partial output of the sentence labeling sub-process. ## 5 Conclusions

In this paper, we proposed a method for (i) identifying novel information from newspaper articles with respect to another article on the same topic, and (ii) making such information available in an easy-to-read format to users. We consider the setting in which there is one reference article (i.e., the article read by a user), and one or more target articles on the same topic.