## 6 Applying the Transformer-Based Models to Twitter Data
### The Results for the Three Languages  
We have received 537 pools and summarizations for English, 422 for French, and 22 for German. Examples of the summarizations are presented in Table 6.  
As the Table 6 demonstrates, the English-language summarizations were clearly understandable. They conveyed the main discussion message, were long enough where the main message was news-like and shorter where it was opinionated. Moreover, in English, it was possible to detect via summarizations when and how the initial outburst of news spread on Charlie Hebdo changed to a real discussion where related issues started to be mentioned and formulated (see Table 7).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**English** & **French** & **German** \\ \hline
7 January 2015, 12:16 the & 7 January 2015, 12:03 Le mardi, a & Jan 07, 12:24 RT und klllint nich \\  & terrorist attack on the offices of the satrical magazine Charlie & Bluvelles, il y a une minute de & nur shnlich \(\ldots\) Du caricaturiste \\  & Hebdo has left 12 dead. & silence pour & haitien [PERSON] \\ \hline
7 January 2015, 12:31 this is a & 7 January 2015, 12:28 Le hashtag & 7 January 2015, 12:20 Anschlag \\  & tribute to the 12 people who & \#usiucharile est le plus utilize de & auf Satrie-Zeitung: [PERSON] \\  & were killed in the attack on the & toute l’histoire de & rettet sich in Bunker \\ \hline
7 January 2015, 12:33 the attack & & & \\ on the offices of the satrical & & & \\ magazine Charlie Hebdo has & 7 January 2015, 12:29 Le dessin de & 7 January 2015, 21:17 Der Pariser \\  & left 12 people dead and & [PERSON] s’etient en homage aux 12 & Anschlag ist auch auf Facebook \\  & 12 others wounded. & & und Twitter das grosse Thema. Irr \\ \hline
7 January 2015, 12:36 we are & 7 January 2015, 12:32 Nouveau: & 8 January 2015, 01:27 Cartoonist \\  & [PERSON]. & homage à [PERSON] et & [PERSON]. “Wir trauern um \\ \hline \hline \end{tabular} Note. Data partly mentioned in [64]. Yellow: unfinished sentences marked.  
\end{table}
Table 6: Examples of text summarizations for Twitter in three languages.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Time** & **Summarization** & **News/Issue** \\ \hline
07-01-2015 8:51 & this is a tribute to the 12 people killed in the terrorist attack on the satrical & News \\  & magazine in Paris today. & & News \\ \hline
07-01-2015 12:26 & the terrorist attack on the offices of the satrical magazine Charlie Hebdo & News \\  & has left 12 dead. & & \\ \hline
07-01-2015 12:28 & the attack on the satrical magazine Charlie Hebdo in the heart of the & & \\  & capital of france is being described as a terrorist attack on freedom & Issue \\  & of expression. & & \\ \hline
07-01-2015 12:29 & freedom of speech is a powerful thing. & Issue \\ \hline
07-01-2015 12:31 & this is a tribute to the 12 people who were killed in the attack on the & News \\  & satrical magazine. & & News \\ \hline
07-01-2015 12:33 & the attack on the offices of the satrical magazine Charlie Hebdo has left 12 & News \\  & people dead and 12 others wounded. & & News \\ \hline
07-01-2015 12:34 & this is a tribute to the 12 people killed m the terrorist attack on the satrical & News \\  & magazine on the 12 th of January 2015. & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Gradual growth of issue-oriented discussion detected via text summarization.  
However, the summarizations for German and French returned understandable but broken sentences with loose endings (for examples, see Table 6, marked yellow). Additional runs did not bring better results. This may have been caused by the fact that Twitter breaks too-long user posts into two parts, and, thus, the model captures the broken structure of tweets. However, random checks of the subsets have not proved this assumption. This means that, most probably, the T5 model for languages other than English needs not only fine-tuning, but also additional training for proper grammar structures.