基于句子重写和重排的文档级释义生成
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering
摘要：
释义生成是自然语言处理中的一项重要任务。以往的研究主要集中在文本级的释义生成，而忽略了文档级的释义生成，这是一个更具挑战性和价值的任务。在本文中，我们首次探讨了文档级释义生成的任务，并通过考虑句子重写和重新排序来关注句子间的多样性。我们提出了CoRPG（Coherence Relationship Guided Paraphrase Generation），它利用图GRU对连贯关系图进行编码，得到每个句子的连贯感知表示，该表示可用于重新排列多个（可能修改的）输入句子。我们创建了一个伪文档级的释义数据集来训练CoRPG。自动评估结果显示，CoRPG在BERTScore和多样性得分上优于几个强基线模型。人工评价也表明我们的模型可以生成更多样性和语义保持的文档释义。
英文摘要：
Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper, we explore the task of document-level paraphrase generation for the first time and focus on the inter-sentence diversity by considering sentence rewriting and reordering. We propose CoRPG (Coherence Relationship guided Paraphrase Generation), which leverages graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence, which can be used for re-arranging the multiple (possibly modified) input sentences. We create a pseudo document-level paraphrase dataset for training CoRPG. Automatic evaluation results show CoRPG outperforms several strong baseline models on the BERTScore and diversity scores. Human evaluation also shows our model can generate document paraphrase with more diversity and semantic preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
