Abstract
Automatic topic-to-essay generation is a challenging task since it requires generating novel,
diverse, and topic-consistent paragraph-level
text with a set of topics as input. Previous
work tends to perform essay generation based
solely on the given topics while ignoring massive commonsense knowledge. However, this
commonsense knowledge provides additional
background information, which can help to
generate essays that are more novel and diverse. Towards filling this gap, we propose
to integrate commonsense from the external
knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve
topic-consistency. We also develop a series
of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training,
the generated essays are more novel, diverse,
and topic-consistent than existing methods in
terms of both automatic and human evaluation.