  Generating a vivid, novel, and diverse essay with only several given topic
words is a challenging task of natural language generation. In previous work,
there are two problems left unsolved: neglect of sentiment beneath the text and
insufficient utilization of topic-related knowledge. Therefore, we propose a
novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge
Graph enhanced decoder, named SCTKG, which is based on the conditional
variational autoencoder (CVAE) framework. We firstly inject the sentiment
information into the generator for controlling sentiment for each sentence,
which leads to various generated essays. Then we design a Topic Knowledge Graph
enhanced decoder. Unlike existing models that use knowledge entities
separately, our model treats the knowledge graph as a whole and encodes more
structured, connected semantic information in the graph to generate a more
relevant essay. Experimental results show that our SCTKG can generate sentiment
controllable essays and outperform the state-of-the-art approach in terms of
topic relevance, fluency, and diversity on both automatic and human evaluation.

Computation and Language