Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Table 2: Performance of topic description generation in terms of automatic metrics.
['Methods', 'B-1', 'B-2', 'R', 'C', 'M']
TAVST achieves higher performance compared to TAVST w/o IU, indicating that the generated story is able to provide assistance for better topic description generation. In general, the description generator obtains low scores in terms of automatic metrics. observations on the dataset reveal that the length of titles for each album is relatively short, ranging from 2 to 6 words mostly. Given such a short reference, it is difficult for models to obtain high scores in terms of automatic metrics. We further look into the generated descriptions and some of them are actually semantically correct. For example, the reference is “happy birthday party at my home” and the generated topic description is“the birthday gathering”. Another example is that the reference is “family feast” and the generated topic is “dinner party”. We believe such kind of topic description with similar meaning can still provide positive guidance for the story generator.