## 7 Conclusions
### Putting Our Findings in Context
The results for the real-world English-language data beyond the existing datasets were a highly satisfactory human assessment, namely, being clearly understandable, mirroring the contents, and capturing the posts' main points, argumentation, and emotions. They also went beyond the baseline results in terms of ROUGE-based assessment. The results were also compared to BART and were better, both in comparison to BART and the non-fine-tuned models. The difference reached 1.2% and 2% for LongFormer and T5, respectively. This is in line with the results of fine-tuning for other types of textual data; however, given that the user-generated texts are noisy and less structured, this result is well beyond what was expected. 2. A new dataset was collected to test the chosen model on a real-world pool of Reddit data for both posts and their respective comments (see Appendix A), available for other scholars on request. 3. Application of the fine-tuned T5 model to the real-world Reddit data of this new dataset has shown that the received summaries are lexically and grammatically satisfactory. They mirror the general meaning of the texts, as well as the authors' sentiment and argumentation patterns. However, the summarizations are much closer to the general meaning of the posts than to the posts' TL;DRs, which poses a fundamental question on whether at all the quality of summaries can be the target of testing, and whether deep learning needs to orient to TL;DRs from Reddit. Several more shortcomings have been noted, among them, an inclination towards extractive summarization and excessive generalization. 4. The fine-tuned model was also applied to comment pools of a significantly more complicated structure than average user posts on Reddit. For some pools, the results obtained resemble those for the posts, which opens wide possibilities for analysis of text pools from social media. However, many pools returned insufficient summarizations that did not correspond to the contents of comments. In many cases, finding a summary for a long a diverse pool created by many users just did not seem feasible, even for a human summarizer; in other cases, a major emotion or a major opinion was detected correctly, in a generalized way; in some rare cases, minor features of comments were picked up. Due to this, for comment sections, it is advised to add a proper comment pooling procedure--affordances-based or computational. 5. The varying length of Reddit comments could be a reason for limited success in comment summarization. This is proved by the fact that summaries of short text pools from microblogs have returned better results. Testing on pools of tweets in English has given satisfactory results by human assessment, allowing for fine-grained detection of agenda shifts and topicality evolution in networked discussions, as well as for an array of other possible applications. However, LongFormer has worked better than T5 for tweet pools, unlike for Reddit. This tells us that, for the same language, platform affordances that define the length and structure of social media texts may demand both different summarization models and additional fine-tuning. This also provides the first explanation of the current impossibility to reach the 'one-for-all' summarization model for all social media that is demanded by the market and some academics. 6. The results of testing T5 on German and French tweet pools, though, has revealed unsatisfactory representations of tweet pools, which might be an artifact of data collection but, with higher probability, it is the result of the models being not fine tuned to languages other than English or to social media data. This demands further fine-tuning of the Transformer-based models for social media studies in other languages. This provides the second explanation for unavailability of the aforementioned 'one-for-all' model. In addition to this, we would like to especially underline that LongFormer is often considered to perform worse than full-fledged hard BERT-based models.