Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Table 5: Sentiment scores corresponding to different types of events. Note that higher score indicates more positive polarity.
['Method', 'Positive new year’s', 'Positive sporting', 'Negative breaking', 'Negative car']
For example, the sentiment of stories about new year’s eve are more likely to be positive while the sentiment of stories about breaking up are more likely to be negative. We enumerate albums with different event types to see if the model has the ability to generate stories with sentiment consistent with the type of events. For each story, we add all the sentiment scores of sentences as its final score. The higher score a story obtain, the more positive the story is. Four types of events are considered. In general, all automatic models tend to generate stories with higher sentiment scores compared to human-written stories. This is because a large portion of albums in the dataset are related to positive events. Both VST and AREL generate stories with similar sentiment scores for both types of events. This indicates that they are not able to distinguish positive and negative events. With the guidance of topic description, our model TAVST is able to distinguish events with different sentiment tendency.