## 1 Introduction  
Nowadays, social networks play an increasingly big role in social and political life, with prominent examples of user discussions shaping public opinion on a variety of important issues. Extraction of public views from social media datasets is a goal that unites sociolinguistic tasks such as sentiment analysis, emotion, and topic extraction, finding product-related recommendations, political polarization analysis, or detection of hidden user communities may be performed via text classification, fuzzy or non-fuzzy clustering, text summarization, text generation, and other techniques of information retrieval. At their intersection, study areas such as sentiment analysis or topic modeling studies have formed. In particular, topicality extraction is a task that most often employs probabilistic clustering such as topic modeling [1; 2; 3], its extensions, or combinations with sentiment analysis [4] or other classes of tasks. However, topic modeling, despite its wide area of application, has many well-known disadvantages. Among them is topic instability [5; 6; 7], difficulties with interpretation [8], artifacts of topic extraction [9], and low quality of topicality extraction for very short texts that need to be pooled to receive distinguishable topics [10]. However, an even bigger problem lies in the fact that, for fast enough assessment of topicality, topic modeling may not fit, as it provides only hints to topics via top words but does not summarize the meaning of the texts relevant to a given topic. Nor does it help outline the themes of specific elements of networked discussions, such as comment threads or 'echo chamber' modules. Moreover, topic modeling takes the dynamics of discussions for statics of a collected dataset, and even topic evolution studies do not allow for clear extraction of particular meanings of discussion fragments and cannot trace how exactly the discussion themes change [11]. This is just one example of how text summarization could have helped social media studies. Thus, it is regrettable that text summarization techniques are not developed for it. Their lack is even more regrettable as, today, a range of new-generation deep learning models for text summarization are available that provide for significantly better results for summarization of structurally diverse and noisy texts. Of them, transfer-learning Transformer-based models have produced state-of-the-art results (see Section 2.2). They, though, need to be adapted for tasks relevant for social media datasets and fine-tuned with regard to the specific features of the data such as use of specific language and platform affordances that shape the data properties, e.g., short/varying length, high level of noise, presence of visual data, etc. Here, the advantage is that, for neural-network text summarization models, problems similar to those in topic modeling may be resolved somewhat easier, by pre-training and fine-tuning instead of development of specialized models of model extensions. Despite this, we still have no clear understanding which Transformer-based model(s) work best with social media data, due to the scarcity of comparative research. Each of these models has notable drawbacks and need pre-testing and fine-tuning for social media data; only after such fine-tuning may these models be compared in quality. This is the first research gap, which we identify and address regarding these new models that may be used for text summarization, such as those based on BART and Transformer architectures, which have not yet been compared after their fine-tuning for particular types of data. For some time already, text summarization studies have used text collections from Reddit. Among different social media platforms, Reddit is undeniably one of the most prominent platforms. First, it differs from others thanks to higher structural heterogeneity of discussion groups (subreddits), which also implies opinion diversity.