论文标题
文本摘要的一般上下文化重写框架
A General Contextualized Rewriting Framework for Text Summarization
论文作者
论文摘要
文本摘要的重写方法结合了提取和抽象的方法，使用抽象模型提高了提取性摘要的简洁性和可读性。退出重写系统将每个提取句子作为唯一的输入，它相对集中，但可能会失去必要的背景知识和话语上下文。在本文中，我们研究了上下文化的重写，该重写会消耗整个文档并考虑摘要上下文。我们将上下文重写正式化为具有组标签对齐方式的SEQ2SEQ，将组标签引入了模拟对齐方式的解决方案，并通过基于内容的地址来识别提取句子。结果表明，我们的方法极大地胜过非上下文的重写系统，而无需加强学习，从而在多个提取器上实现了胭脂分数的强烈改进。
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.
