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

One of the most pressing issues that have arisen due to the rapid growth of
the Internet is known as information overloading. Simplifying the relevant
information in the form of a summary will assist many people because the
material on any topic is plentiful on the Internet. Manually summarising
massive amounts of text is quite challenging for humans. So, it has increased
the need for more complex and powerful summarizers. Researchers have been
trying to improve approaches for creating summaries since the 1950s, such that
the machine-generated summary matches the human-created summary. This study
provides a detailed state-of-the-art analysis of text summarization concepts
such as summarization approaches, techniques used, standard datasets,
evaluation metrics and future scopes for research. The most commonly accepted
approaches are extractive and abstractive, studied in detail in this work.
Evaluating the summary and increasing the development of reusable resources and
infrastructure aids in comparing and replicating findings, adding competition
to improve the outcomes. Different evaluation methods of generated summaries
are also discussed in this study. Finally, at the end of this study, several
challenges and research opportunities related to text summarization research
are mentioned that may be useful for potential researchers working in this
area.

本文详细介绍了文本摘要的各种方法，包括提取和抽象两种途径，评估指标、基准数据集与未来的研究展望，并讨论了生成摘要的不同评估方法与相关研究中可能出现的挑战和研究机遇。