## 5 The Model Application to Real-World Reddit Posts and Comments  
### Applying the Model to the Dataset of Unlabeled Reddit Posts  
The chosen model has been tested on new data to show its efficiency on the previously unseen data. In our previous practice of working with automated text analysis, we have seen multiple times that the models that are believed to work well by automatic metrics do not really provide for satisfactory results when the result is human assessed. This is why we have applied the fine-tuned T5 model to the unlabeled data, to assess by human eye the user summaries (TL;DRs) vs. machine summaries and reveal possible shortcomings. We especially underline here that the Transformer-based models especially fine-tuned for \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Metrics**} & **BART** & **T5** & **LongFormer** \\ \hline \multirow{3}{*}{ROUGE-1} & average & 20.891 & 20.972 & 20.946 \\  & minimum & 20.687 & 20.802 & 20.925 \\  & maximum & 20.962 & **21.051** & 20.972 \\ \hline \multirow{3}{*}{ROUGE-2} & average & 20.025 & 20.189 & 20.125 \\  & minimum & 19.725 & 20.005 & 20.042 \\  & maximum & 20.085 & **20.265** & 20.144 \\ \hline \multirow{3}{*}{ROUGE-L} & average & 20.781 & 20.896 & 20.865 \\  & minimum & 20.501 & 20.735 & 20.821 \\ \cline{1-1}  & maximum & 20.853 & **20.977** & 20.896 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of fine-tuning, as measured by the ROUGE metrics. Figure 3: The results of fine-tuning, as evaluated by the ROUGE metrics. a certain type of data (Reddit) have not been previously tested on the raw data from the platform; before our tests, only non-fine-tuned models were applied to massive Reddit datasets, and the results were compared to data of a different nature, which did not lead to better performance for opinion mining from social media. We have obtained the data from 'r/wallstreetbets' subreddit, one of the most popular on Reddit (of over 10 million users). Recently, it has been the origin point of a major social-economic discussion involving stock trades. We have collected this subreddit in order to test the model on the data that do not belong to any existing dataset specifically prepared for summarization tests; thus, the collected data may be called real-world, unlabeled, or raw data. The top posts were collected, most of them dated circa mid-2020 (around the time of the highest user engagement). A total of 150,000 posts were collected. First, we received the automated summaries and assessed them per se (as sentences) in the working group that contained experienced Internet researchers, communication scientists, and linguists. Here, we need to note that not many posts on Reddit have the TL;DR addition; this is why it is important to assess the automated summaries vs. both the posts themselves, not only vs. TL;DRs. We have assessed both. When juxtaposed to the posts themselves, the summaries were coherent short sentences with clear relation to post title (see Table 3); although, the extent of this connection requires further automated testing. The summaries correctly reflected the nature of posts, including the first-person speech ('I hate the market... '), user's opinion/position ('Weird price is now around where the volumes dropped off'), argumentation structure ('This post is not about the fundamentals in the first place'), and emotions ('The basators who steal thousands of years off the regular man's lives... '). There were some shortcomings in logic ('I hate the market, and I don't think we need to talk to each other') and grammar, e.g., some rare statements with no predicate. We have also spotted some hint of extraction, rather than abstraction, for shorter texts where key short phrases were added to abstractive parts of summaries ('There are a lot of factors out here. It is not a meme.'). In addition, when the posts were changed many times (e.g., had 'Edit 5', 'Edit 6', etc.)