## 4 Results
### Discussion Related to Evaluation Metrics  
This section discusses the obtained scores for all the evaluation metrics for generated texts mentioned in the previous section. The Tables 14 and 15 represents the metrics related to LSTM model based generated text. Whereas, Table 16 represents the metrics related to GPT-2 based generated text. If we look into all the three tables, the first three metrics (BLEU, METEOR and ROUGE_L) have very low score values for the generated texts with reference to original texts. However, the next two metrics (Skip-Thought, Embedding-Average) represents high score values for the generated texts with reference to the original texts. The possible reason for low score values for the first three metric is due to less number of overlapping words in the generated texts with reference to original texts. But, the generated texts of the specific class label is relevant. Tables 17 and 18 shows the number of overlapping words (i.e., bold words) in generated as well as original texts for different class labels. The tables shows that the number of overlapping-words are less in number but still the generated texts represents sense of relevant class labels. However, when we calculated second set of metrics (i.e., embedding-based metrics) we observed significant improvement in the calculated scores. These scores represent the semantic similarity of the generated texts with reference to original texts. Hence, the scores give evidence that the generated text is relevant to specific class labels. Another, point that needs to be discussed here is we know that the Tables 14 and 15 show scores for LSTM based text generation model and the Table 16 shows scores for GPT-2 \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metrics** & **Arts and** & **Business** & **Computer** & **Data** & **Health** & **Information** & **Social** \\  & **Humanities** & **Science** & **Science** & **Science** & **Technology** & **Sciences** \\ \hline BLEU & 0.029 & 0.046 & 0.034 & 0.035 & 0.028 & 0.041 & 0.022 \\ METEOR & 0.128 & 0.151 & 0.114 & 0.128 & 0.107 & 0.147 & 0.105 \\ ROUGE\_L & 0.124 & 0.125 & 0.124 & 0.126 & 0.116 & 0.126 & 0.113 \\ Skip-Thought & 0.435 & 0.481 & 0.461 & 0.473 & 0.468 & 0.472 & 0.439 \\ Embedding-Average & 0.964 & 0.982 & 0.983 & 0.977 & 0.961 & 0.975 & 0.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Evaluation Metrics Scores (MOOCs Lecture Transcripts Dataset [26]). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**\#** & **Original Text** & **Generated Text** & **Class Label** \\ \hline
1 & & & I want to complain to the rest of & \\  & & & my since people and walk by the & \\
1 & & I just absolutely **lst my mind.** (i & times again. What is your face & \\  & & that so I don’t understand or & \\  & & that so I don’t understand or & \\  & & & **lst my mind down** & \\  & & wedding makes headlines and & \\
2 & & provides that pathetic excuse of & \\  & & a celebrity **with more money** & & \\  & & makes me sick & & \\  & & Don’t believe the less look me & \\
3 & & In the eyes, please don’t be & \\  & & scared of me & \\
4 & & I miss hanging out with [PERSON], & \\  & & [PERSON] and lay. Those were **good times** & \\  & & You would think by now they & \\
5 & & would of controlled the smoke & \\  & & problem in the **bathrooms** & \\  & & Butttit, they haven’t. & \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Original + Generated Texts Sample from Twitter Emotion Dataset [16].