## 5 Experiments and Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{3}{c}{**ROUGE-2**} & \multicolumn{3}{c}{**ROUGE-L**} \\ \cline{2-7}  & **Precision** & **Recall** & **f-Score** & **Precision** & **Recall** & **f-Score** \\ \hline EN & **0.456 \(\pm\) 0.020** & **0.474 \(\pm\) 0.015** & **0.459 \(\pm\) 0.016** & 0.526 \(\pm\) 0.011 & **0.558 \(\pm\) 0.008** & **0.520 \(\pm\) 0.007** \\ \hline LT & **0.431 \(\pm\) 0.043** & **0.431 \(\pm\) 0.044** & **0.428 \(\pm\) 0.043** & **0.497 \(\pm\) 0.040** & **0.515 \(\pm\) 0.040** & **0.488 \(\pm\) 0.041** \\ \hline ft\_encoder\_EN & 0.346 \(\pm\) 0.028 & 0.363 \(\pm\) 0.025 & 0.349 \(\pm\) 0.026 & 0.429 \(\pm\) 0.024 & 0.464 \(\pm\) 0.021 & 0.425 \(\pm\) 0.022 \\ \hline ft\_encoder\_LT & 0.380 \(\pm\) 0.043 & 0.389 \(\pm\) 0.045 & 0.381 \(\pm\) 0.044 & 0.454 \(\pm\) 0.039 & 0.482 \(\pm\) 0.037 & 0.451 \(\pm\) 0.038 \\ \hline ft\_encoder\_decoder\_EN & 0.290 \(\pm\) 0.018 & 0.362 \(\pm\) 0.049 & 0.312 \(\pm\) 0.027 & 0.388 \(\pm\) 0.023 & 0.479 \(\pm\) 0.049 & 0.397 \(\pm\) 0.030 \\ \hline ft\_encoder\_decoder\_LT & 0.287 \(\pm\) 0.018 & 0.411 \(\pm\) 0.029 & 0.328 \(\pm\) 0.022 & 0.374 \(\pm\) 0.026 & 0.508 \(\pm\) 0.027 & 0.393 \(\pm\) 0.026 \\ \hline BERT\_encoder\_EN & 0.428 \(\pm\) 0.012 & 0.426 \(\pm\) 0.007 & 0.421 \(\pm\) 0.009 & 0.511 \(\pm\) 0.020 & 0.525 \(\pm\) 0.011 & 0.494 \(\pm\) 0.013 \\ \hline BERT\_encoder\_LT & 0.401 \(\pm\) 0.028 & 0.417 \(\pm\) 0.026 & 0.404 \(\pm\) 0.028 & 0.464 \(\pm\) 0.022 & 0.503 \(\pm\) 0.020 & 0.460 \(\pm\) 0.022 \\ \hline BERT\_encoder\_decoder\_EN & 0.455 \(\pm\) 0.012 & 0.454 \(\pm\) 0.020 & 0.448 \(\pm\) 0.016 & 0.531 \(\pm\) 0.007 & 0.546 \(\pm\) 0.015 & 0.513 \(\pm\) 0.012 \\ \hline BERT\_encoder\_decoder\_LT & 0.409 \(\pm\) 0.027 & 0.423 \(\pm\) 0.028 & 0.410 \(\pm\) 0.028 & 0.473 \(\pm\) 0.021 & 0.513 \(\pm\) 0.024 & 0.471 \(\pm\) 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Averaged ROUGE-2 and ROUGE-L precision/recall/f-score values with confidence intervals using the LSTM encoder and decoder model with different vectorization types on the dataset with separated punctuation. The best values in the column for EN and LT are in bold. Despite the used punctuation treatment, the highest BLUE values (as presented in Figures 5 and 6) with the simple LSTM encoder architecture were achieved using one-hot encoding vectorization for both encoder and decoder. FastText vectorization is recommended only for the encoder and only for the Lithuanian language. Differences in BLUE scores are not so huge with the BERT vectorization, whether for different languages or for different vectorization units (BERT for encoder; BERT for encoder plus decoder). In Tables 2 and 3, we see the same trend as in Figures 5 and 6, respectively. Hence, one-hot encoding vectorization for both units is the best solution with the simple LSTM encoder-decoder architecture for both English and Lithuanian languages. FastText vectorization with the encoder and decoder units is not recommended. The BLUE scores with the LSTM2 encoder and decoder model applied with different vectorization types to the datasets with removed and separated punctuation are presented in Figures 7 and 8, respectively. The ROUGE-2 and ROUGE-L precision/recall/f-score values are presented in Tables 4 and 5, respectively. When comparing Figure 7 with Figure 8 representing the BLUE scores with the stacked LSTM encoder, it is difficult to draw very firm conclusions. One-hot encoding for both encoder and decoder units remains the best vectorization technique on the English dataset with the separated punctuation. However, if excluding this particular result, BERT vectorization (for the encoder or encoder plus decoder) stands out with the best achieved BLUE values for both languages. Surprisingly, with the separated punctuation and BLUE vectorization, the results on the Lithuanian dataset are even better.