## 5 The Model Application to Real-World Reddit Posts and Comments
### Applying the Model to the Dataset of Unlabeled Reddit Posts
or had many multi-vector reasons the author gave to argue his/her position, the machine tended to summarize with excessive generalization ('There are a lot of factors here'; 'There is a good reason for GME'). However, the summaries, in general, mirrored the contents of the Reddit posts correctly and to the point. For examples of posts with no TL;DR and their summaries, see Appendix A. More surprising results were brought when we have tried to compare the automated summaries to user TL;DRs (see Table 4). The number of the posts with TL;DRs was very small and, thus, could be closely assessed by the experts. The machine summaries \begin{table}
\begin{tabular}{l l} \hline \hline
**Post Title** & **Automated Summary** \\ \hline Daily reminder that buying GME at 300 before & This post is not about the fundamentals in the first place, it was a risky bet based on the \\
28/1 was a great play at the time & Incoming short squeeze. \\ \hline Why should any American company ever act & I hate the market, and I don’t think we need to \\ responsibly again? & talk to each other \\ \hline At these levels, it’s NOT about the roe of the stock. its the number of shares in the hedge & Weird price is now around where the volume \\ funds’ possession. & dropped off. I think we’re at the bottom, as \\ \hline People are risking their lives to wage war & The basators who steal thousands of years of \\ against the suits and it brings tears to my eyes & the regular man’s lives every single day. \\ to watch them do it & \\ \hline Pledge to Delete [PERSON] After The Great & There are a lot of factors out there. It’s not \\ Rise. Who’s With Me? & a meme. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of post summaries without TL;DRs. corresponded to the user summaries in only circa 50% of posts. However, this was not due to the low quality of post summarization, but because the Reddit users did not properly use the TL;DR function, making sarcastic, aggressive, or non-sensical claims under 'TL;DR'. Where user summaries truly reflected the essence of the posts, the machine summaries closely resembled the user TL;DRs. However, where the authors used TL;DR for conveying some emotional message, swearing, or slang-based claims, the machine summary could not create a similar one, as it was oriented to the post, not to TL;DR (see Table 4, line 4). In case when the posts consisted of links and its TL;DR had words, the machine returned unreliable results (see Table 4, line 5). This revelation problematizes the Reddit-based summarization research, as it poses a fundamental question: Do we teach the machine to 'ideally' summarize the post, or do we teach it to produce human-like summaries (with all the ironic and unpredictable uses of TL;DRs), as this is not the same? All in all, the fine-tuned T5 model showed its high capacity for summarizing posts correctly and close to human summaries and may be recommended for further practical of the data from Reddit--however, with no orientation to the TL;DR section of the posts, as this section itself might be highly misleading.