## 1 Introduction
As for the quality of sentence-level representations, we considered Sentence-BERT [5]. It is a method used for further fine-tuning a Transformer model that leverages Siamese and triplet network structures, semantic textual similarity, and natural language inferencing training tasks in order to improve the sequence-level representations provided by a Transformer model. Sentence-BERT is specifically designed to derive semantically meaningful sentence embeddings that can be compared using cosine as a similarity metric [5]. This latter aspect is helpful when designing approaches aimed at reducing the computational cost of solving the problem. We can argue that the computational cost of Transformer-based models is a slightly less prominent issue if we consider them only from a feature extraction perspective. Thus, we propose leveraging Sentence-BERT to obtain sentence-level similarity ratings for newspaper articles. We exploit these similarities for the sentence classification task described in Section 3. Specifically, we leverage the cosine similarity between sentences as a proxy of their similarity to classify sentences of the target articles with one of three classes, namely _similar_ (SS), _different_ (DS), and _very different_ (VDS), representing the difference level with sentences in the reference article, based on their cosine similarity. Although our aim is to discriminate between similar and different sentences, we realized that some sentences are neither too similar nor too different. Thus, we introduce an intermediate class that represents sentences that are different, but not distinct enough to be certain that they contain new information with respect to the reference article. Then, we consider all the target articles and employ a similar approach in order to further refine our sentence selection. Specifically, we aim to discard sentences in the target articles that are similar to the sentences in other target articles, in order to reduce the number of overall sentences considered and avoid duplicated information. We describe this process in Section 3.3. Clearly, semantic differences lie on a spectrum and are challenging to reduce to distinct classes. However, we believe that choosing a three-class distinction allows us to avoid performing a too-simplistic distinction between similar and non-similar sentences while giving us enough descriptive power to perform a reliable early analysis of the results. Finally, we employ a Transformer-based abstractive summarization model in order to generate easy-to-read summaries of novel information based on the survived sentences. We describe this process in Section 3.4. We performed a set of experiments aimed at evaluating both the performances of the sentence classification and the quality of the summarization model for our goal. More specifically, we measured the effectiveness of the sentence classification in identifying novel content in target articles, with respect to the reference article by comparison with human judgments. Such judgments were obtained by means of crowdsourcing. This also enabled us to release a dataset of sentences labeled for similarity, which is likely to be very helpful in several tasks. In addition to this, we compared the performance of our system against a BERT-based classifier that was fine-tuned for the same task, and we employed the same BERT model as a frozen model, applying the prompt tuning technique to utilize it to solve the task. We show that our system is able to achieve comparable performance at a fraction of the computational cost. In fact, while the standard classification pipeline is based on (i) fine-tuning the model, or learning soft prompts to condition the frozen model to the classification task, and (ii) producing the results at inference time by feeding all the test data to the trained classifier, our pipeline allows us, on the one hand, to leverage a pre-trained model without additional tuning on the data itself and, on the other hand, to provide the final results by simply considering the similarity matrix between sentences.