The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive model. Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context. In this paper, we investigate contextualized rewriting, which consumes the entire document and considers the summary context. We formalize contextualized rewriting as a seq2seq with group-tag alignments, introducing group-tag as a solution to model the alignments, identifying extractive sentences through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning, achieving strong improvements on ROUGE scores upon multiple extractors.

翻译：文本摘要的重写方法结合了采掘和抽象方法,用抽象模型改进了采掘摘要的简洁性和可读性。退出重写系统将每个提取性句都作为唯一的投入,相对集中,但可能会失去必要的背景知识和讨论背景。在本文中,我们调查背景化重写,因为背景化重写耗尽了整个文件并考虑了摘要背景。我们正式将背景化重写作为后继2当量,与群体级对齐,引入群体式重写作为调整模式的一种解决办法,通过基于内容的地址识别提取性重写。结果显示,我们的方法在不需要强化学习的情况下大大优于非文字化重写系统,在多个提取器的ROUGE分数上取得了很大的改进。