## 7 Conclusions  
### Putting Our Findings in Context  
Opinion mining from social networks is one of most common and useful applications of natural language processing tools. There have been a variety of approaches, models, and datasets designed to work specifically for social networks. This includes topic modeling approaches, extractive and abstractive summarizations, sentiment analysis, and many others. In this work, we mainly focused on abstractive summarization approaches, as this class of models provides great insights for topicality detection in textual data and may be combined with other models. One of the most interesting social media platforms for application of automatic analysis is Reddit. It is unique mainly in its community-driven structure: the website consists of different subreddits divided by topics and opinions created and curated largely by the users themselves. Additionally, its explosive growth in user base and popularity makes opinions shared on the platform a driving factor in some major real-world events. In this study, we show that abstractive summarization models can effectively be applied to analyzing such content, both in terms of original posts and their respective comments. Our contribution to abstract summarization studies is the provision of evidence on the varying quality of neural network models for summarization tasks and selection of the best models. In addition, we went beyond judging the models trained on open-source data and had them tested on real data. Experimental results prove that the LongFormer and T5 models demonstrate the best \begin{table}
\begin{tabular}{l l c} \hline
**Time** & **Summarization** & **News/Issue** \\ \hline
07-01-2015 12:36 & we are all Charlie. & Issue \\ \hline
07-01-2015 12:38 & this is a tribute to the 12 people who were killed in the attack on the & News \\  & satirical magazine Charlie Hebdo. & News \\ \hline
07-01-2015 12:40 & this is a tribute to the 12 people who were killed in the attack on the & News \\  & satirical magazine Charlie Hebdo in france. & News \\ \hline
07-01-2015 12:42 & the attack on the satirical magazine Charlie Hebdo has left 12 people dead & \\  & and 12 others wounded. this is the deadliest terrorist attack in the history & News \\  & of the world. & \\ \hline
07-01-2015 12:43 & this is a tribute to the 12 people who were killed in the terrorist attack on & News \\  & the satirical magazine Charlie Hebdo on 8 th de January 2015. & News \\ \hline
07-01-2015 12:45 & the attack on in was 100\% about religion. & Issue \\ \hline
07-01-2015 12:47 & in the wake of the attack on the satirical magazine, the world is united m & Issue \\  & solidarity with the victims and their families. & \\ \hline
07-01-2015 12:48 & we live in a world where a man with a gun is intimidated by a man with a & \\  & pen. if extremism has taught us anything, its that the pen is midheir than & Issue \\  & rocket launcher. & \\ \hline
07-01-2015 12:50 & freedom of expression is everyone’s basic human right and is one that & \\  & shouldn’t need defending. what happened in France today is unacceptable. & Issue \\ \hline
07-01-2015 12:51 & freedom of speech is the foundation that allows a culture to advance. & Issue \\ \hline
07-01-2015 12:53 & freedom of expression is a gift. & Issue \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._performance as judged by the ROUGE score (around 2% increase in quality in comparison with other fine-tuned models and their baseline results) on the initial data with high quality of subsequent summarizations. This shows higher applicability of the T5 model, and more possibilities for improvement for the LongFormer model. Our main contributions may be formulated as follows:  
1. State-of-the-art Transformer-based models were fine-tuned and tested on open-source-labeled data obtained from Reddit, with an emphasis on tuning the existing models trained on large datasets, to improve their performance when working with social media data.