## 1 Introduction  
Today, many people use social media platforms to acquire information or to spread information around the world. From this perspective, social media platforms are popular as hubs for the collection and enjoyment of news. Thus, social media has become a useful tool for newspapers, which use social media platforms to disseminate news and attract users. These users, in turn, are inundated with a vast array of information, often interpreted from different personal and political perspectives. Newspapers differ from each other in the way they deal with specific topics. It can be difficult for a user to obtain clean and complete information. Different sources could treat the same event in different ways, and some content information could be contradictory. Furthermore, certain newspapers could spread misleading information or fake news [1]. Thus, each news item can contain, in practice, different details on a specific topic. Users seek to gather as much information as possible on a topic to have the opportunity to form their personal opinions. However, they would like to avoid reading all related news items, which often contain redundant information, choosing instead to only focus on content differences. Starting from this observation, we propose a system that takes a newspaper article, denoted as the _reference article_, and retrieves a set of articles, denoted as _target articles_, on the same topic; the system automatically assesses the contents of these articles in order to pinpoint similarities and differences with respect to the reference article by means of sentence-level similarity. The identified differences can be interpreted as novel and possibly interesting content. Our end goal is to provide the users with an abstractive summarization of such content, thus reducing their reading time and effort. The system is based on a methodology that leverages the similarity between sentences to find groups of them with varying degrees of similarity. In order to define and exploit a metric for determining sentence similarity, we turn to the literature on natural language processing (NLP), particularly distributional semantics. Most modern approaches in this area are based on neural language models and, more recently, Transformer architectures. At their core, such models define a data-driven probability distribution over sequences of tokens. A token is typically an approximation of the concept of a word and a discrete entity in texts. This distribution is used to learn a fixed-length representation of textual units in a latent space. Distances between such representations in the space can be used to approximate semantic similarity by exploiting the so-called distributional hypothesis [2], which states that similarities in distribution tend to have similar meanings. Thus, similar representations reflect similar word meanings. In recent years, Transformer architectures such as BERT [3] have established themselves as the de facto standard, obtaining state-of-the-art results in many NLP-related tasks, including token-level contextualized representations. In the context of sentence-level comparisons, we can identify two major shortcomings in employing a standard classification approach. First, as the authors of BERT pointed out, sequence-level representations are not to be considered as semantically relevant for determining similarity between sentences [3]. Indeed, token-level representations extracted with BERT-like models tend to obtain results that are generally on par with--if not better than--more traditional approaches, such as word2 vec [4] for token-level semantic similarity. Nevertheless, sequence-level representations are typically learned as feature vectors for subsequent classification tasks, thus BERT-like models are not very reliable for determining similarity between sentences. Second, employing a classification model for such a problem may prove to be a rather costly approach. Transformer-based neural language models typically require GPU acceleration during the training phase.