In this paper, we explore \textit{summary-to-article generation}: the task of generating long articles given a short summary, which provides finer-grained content control for the generated text. To prevent sequence-to-sequence (seq2seq) models from degenerating into language models and better controlling the long text to be generated, we propose a hierarchical generation approach which first generates a sketch of intermediate length based on the summary and then completes the article by enriching the generated sketch. To mitigate the discrepancy between the ``oracle'' sketch used during training and the noisy sketch generated during inference, we propose an end-to-end joint training framework based on multi-agent reinforcement learning. For evaluation, we use text summarization corpora by reversing their inputs and outputs, and introduce a novel evaluation method that employs a summarization system to summarize the generated article and test its match with the original input summary. Experiments show that our proposed hierarchical generation approach can generate a coherent and relevant article based on the given summary, yielding significant improvements upon conventional seq2seq models.
we explore the task of summary-to-article generation and propose a hierarchical generation scheme together with a jointly end-to-end reinforcement learning framework to train the hierarchical model. To address the issue of degeneration in summary-to-article generation, this paper proposes a hierarchical generation approach which first generates an intermediate sketch of the article and then the full article.