Hierarchical Summary-to-Article Generation
In this paper, we explore : 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 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.