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.

通过将抽取和生成的方法相结合，利用生成模型提高了抽取式摘要的简洁性和可读性。本研究提出了上下文感知的重写方法，通过 group-tag alignments 将传统的抽取方式转化为生成方式，取得了显著的 ROUGE 分数提升。

文本摘要的通用上下文重写框架

A General Contextualized Rewriting Framework for Text Summarization

Extractive summarization suffers from irrelevance, redundancy and
incoherence. Existing work shows that abstractive rewriting for extractive
summaries can improve the conciseness and readability. These rewriting systems
consider extracted summaries as the only input, which is relatively focused but
can lose important background knowledge. In this paper, we investigate
contextualized rewriting, which ingests the entire original document. We
formalize contextualized rewriting as a seq2seq problem with group alignments,
introducing group tag as a solution to model the alignments, identifying
extracted summaries 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 extractive summarizers.

本文探讨了在提取式摘要中基于全文进行 contextualized rewriting，将其形式化为 seq2seq 问题，引入 group tag 作为解决方案来建立关联，通过基于内容的寻址方法识别提取式摘要，结果表明我们的方法明显优于非上下文的重写系统，无需采用强化学习，在多个提取式摘要生成器上显示出显着提高的 ROUGE 分数。