  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.
