A Planning based Framework for Essay Generation
This is an incremental approach to automated essay generation for natural language processing applications.
The paper tackles essay generation by developing a framework based on text planning with components for topic understanding, sentence extraction, and reordering, using statistical algorithms and testing on Chinese corpus, but it does not report specific numerical results.
Generating an article automatically with computer program is a challenging task in artificial intelligence and natural language processing. In this paper, we target at essay generation, which takes as input a topic word in mind and generates an organized article under the theme of the topic. We follow the idea of text planning \cite{Reiter1997} and develop an essay generation framework. The framework consists of three components, including topic understanding, sentence extraction and sentence reordering. For each component, we studied several statistical algorithms and empirically compared between them in terms of qualitative or quantitative analysis. Although we run experiments on Chinese corpus, the method is language independent and can be easily adapted to other language. We lay out the remaining challenges and suggest avenues for future research.