  Telling stories is an integral part of human communication which can evoke
emotions and influence the affective states of the audience. Automatically
modeling emotional trajectories in stories has thus attracted considerable
scholarly interest. However, as most existing works have been limited to
unsupervised dictionary-based approaches, there is no benchmark for this task.
We address this gap by introducing continuous valence and arousal labels for an
existing dataset of children's stories originally annotated with discrete
emotion categories. We collect additional annotations for this data and map the
categorical labels to the continuous valence and arousal space. For predicting
the thus obtained emotionality signals, we fine-tune a DeBERTa model and
improve upon this baseline via a weakly supervised learning approach. The best
configuration achieves a Concordance Correlation Coefficient (CCC) of $.8221$
for valence and $.7125$ for arousal on the test set, demonstrating the efficacy
of our proposed approach. A detailed analysis shows the extent to which the
results vary depending on factors such as the author, the individual story, or
the section within the story. In addition, we uncover the weaknesses of our
approach by investigating examples that prove to be difficult to predict.
