# Abstractive Summarizers Become Emotional on News Summarization
## 5 Emotions in Summarization Corpora  
First, we study how frequently the articles and summaries contain emotional words. To this aim, Table 3 shows the percentage of articles and summaries that has at least one word of an emotion. Most articles in both corpora show some emotion, and it is common to see all the emotions co-occurring (77% of articles in Xsum and 95% in Cnn/Dailymail have words representing all the emotions at some point). It is not so in the summaries: the percentage of summaries that elicit each emotion is lower than the percentage of articles, especially in Xsum, and it is not as frequent as in the articles where all the emotions co-occur. _Fear_, _sadness_, _anticipation_, and _trust_ are the emotions that appear in a more significant number of articles and summaries. We carried out a study of the most frequent combination of emotions in the summaries. The study shows that there are larger combinations of emotions in Cnn/Dailymail than in Xsum, likely because summaries are twice as long. Interestingly, summaries of CNN/Dailymail are twice as long as Xsum ones, but it is four times more likely that all emotions appear in their summaries (23.08% vs. 5.68%). Of theCNN/DailyMail summaries, 52.43% are in the top-10 combinations, while, in Xsum, the top-10 combinations accumulate 29.74% of the summaries. Figures 13 and 14 of Appendix A show this study. We found 27.8k examples in Xsum (12%) and 2.8k in Cnn/Dailymail (0.9%) where the reference summaries elicit at least one emotion that does not appear in the article. For both corpora, the most frequent emotions in these cases are _disgust_, _anger_, and _surprise_, and the least frequent ones are _anticipation_ and _trust_. Table 13 of Appendix B shows one example from Xsum. Second, by focusing on emotion densities and ratios, we study how frequently each emotion is elicited in articles and summaries and what emotions are over/under-emphasized in the summaries. Figure 1 shows Kernel Density Estimation (KDE) plots of emotion densities and ratios for each emotion in both corpora. In these \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multirow{2}{*}{**\#docs**} & \multicolumn{2}{c}{**avg. doc. Length**} & \multicolumn{2}{c}{**avg. sum. Length**} & \multicolumn{2}{c}{**Vocabulary Size**} \\ \cline{3-8}  & & **Words** & **Sentences** & **Words** & **Sentences** & **Document** & **Summary** \\ \hline Cnn/Dailymail & 311,971 & 693.62 & 38.55 & 49.00 & 3.70 & 839,788 & 231,778 \\ Xsum & 226,711 & 377.51 & 19.20 & 21.33 & 1.00 & 425,532 & 83,414 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics for the two corpora: Cnn/Dailymail and Xsum. From left to right: corpus size, average document, and summary length (in terms of words and sentences), and vocabulary size in document and summary. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Fear** & **Anger** & **Anticipation** & **Trust** & **Surprise** & **Sadness** & **Disgust** & **Joy** \\ \hline \multirow{2}{*}{**CNN/DM**} & **\%Docs** & 99.66 & 98.81 & 99.58 & 99.98 & 99.24 & 99.67 & 97.17 & 99.40 \\  & **\%Summs** & 79.22 & 69.63 & 84.23 & 90.12 & 60.36 & 76.38 & 53.81 & 69.79 \\ \hline \multirow{2}{*}{**XSUM**} & **\%Docs** & 95.72 & 91.74 & 98.60 & 99.04 & 93.58 & 96.26 & 85.70 & 94.97 \\  & **\%Summs** & 59.45 & 46.89 & 61.19 & 70.52 & 38.36 & 54.17 & 31.75 & 44.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of articles and summaries in both corpora containing at least one word of an emotion. plots, the x-axes represent values of either emotion densities or ratios, and the y-axes define the probability density function for the kernel density estimation. The figure shows that emotion densities and ratios are similarly distributed. Related to the articles (first column in Figure 1), _trust_ concentrates the most significant number of articles with higher \(ED(e,a)\). In contrast, _disgust_ collects the most significant number of articles with lower \(ED(e,a)\). The distribution of _fear_ is the most skewed.