Topic-to-essay generation with knowledge-based content selection
Jieyong Wang Chunyao Song Yihao Wu
The topic-to-essay generation task is a challenging natural language
generation task that aims to generate paragraph-level text with high semantic
coherence based on a given set of topic words. Previous work has focused on the
introduction of external knowledge, ignoring the insufficient generated text
diversity. In order to improve the generation diversity, we propose a novel
copy mechanism model with a content selection module that integrates rich
semantic knowledge from the language model into the decoder. Furthermore, we
introduce the improved prefix tuning method to train the model, enabling it to
adapt to varying input complexities. In addition, we have contributed a new
Chinese dataset for TEG tasks. Experimental results demonstrate that the
proposed model can improve the generated text diversity by 35\% to 59\%
compared to the state-of-the-art method, while maintaining a high level of
topic consistency.