## 1 Introduction
Second, it has many times sparked large-scale cross-platform discussions leading to cross-national outrage (e.g., on the Boston Marathon Bombing suspects in 2013) and shaped trends in developed economies (e.g., AMC and Gamestop stock investments changing financial strategies of Wall Street). At one point, it used to be the third most popular website in the USA. Third, and most important for text summarization studies, it has platform affordances that demand users create author text summarizations that may be used as target texts. Compared to other discussion platforms such as Twitter or Facebook, Reddit has been under-researched for application of automated text analysis in general, and of transfer learning in particular. Examples of the recent Reddit-based opinion explorations include studying COVID-19 publications using statistical techniques (with Reddit being just one of many datasets) [12], community detection using topic information [13], and applying sentiment analysis to Reddit data [14], but such studies do not seem to form a notable field within opinion mining from social media. On the other hand, for abstractive text summarization studies, Reddit, in effect, is today the only social media platform that has provided multiple datasets of user-generated content--or, more precisely, users' self-authored summaries, thanks to its \"Too Long; Didn't Read\" (TL;DR) feature that allows discussion participants leave human summarizations for posts. Text summarization studies, thus, employ Reddit datasets to test models--but next-to-never are applied to answer sociological or communication science questions with the help of these models, which leaves the question of their applicability open. This is the second research gap that we address. The most recent works that involve Reddit [15; 16] compare deep-learning extractive and abstractive summarization, and abstractive models, especially BART and its extensions, are shown to have the best results in comparison with ground truth. However, newer models such as T5 and LongFormer have not yet been tested on Reddit, even if T5 has already been used in summarization studies [17]. Out of it, one can clearly see that text summarization studies for social media are still at the very early stage of development, and the highly advanced neural-network models that produce good results for other types of textual data are still to be applied to various social media platforms beyond Reddit. We see this as the third research gap. Today, the scarcity of labeled datasets for text summarization studies highly limits the development of text summarization models for other platforms; we will perform a step further here and apply the models to real-world data on Twitter. Moreover, as we have shown above, social media data have a complicated nature. They are not only noisy, but also structurally diverse. What matters sociologically is not only the meaning of single posts but also that of pools of user posts and/or comments that correspond to elements of networked discussion structure. Summarization of text pools exists, but not for social media data; this is the fourth gap that we address. Thus, this study aims at several consecutive steps in expanding today's abstractive summarization studies in their application to social media data. First, it is the fine-tuning and comparative assessment of Transformer-based abstractive summarization models for the Reddit data. As per other scholars, we use Reddit as an exemplary platform, but add T5 and LongFormer models to BERTSum and BART already tested on Reddit. We perform this by utilizing a research pipeline that includes data collection and preprocessing; model loading, fine-tuning, and application; and comparative assessment of the results and their visualization. As stated above, the chosen models are first fine-tuned on a standard open-source-labeled Reddit dataset, to receive the large-scale pre-trained models adapted to social media analysis.