With the explosive growth of textual information, summarization systems have become increasingly important. This work aims to concisely indicate the current state of the art in abstractive text summarization. As part of this, we outline the current paradigm shifts towards pre-trained encoder-decoder models and large autoregressive language models. Additionally, we delve further into the challenges of evaluating summarization systems and the potential of instruction-tuned models for zero-shot summarization. Finally, we provide a brief overview of how summarization systems are currently being integrated into commercial applications.

翻译：随着文本信息的爆炸式增长，摘要系统变得愈发重要。本文旨在简明扼要地阐述生成式文本摘要领域的最新发展现状。为此，我们概述了当前范式向预训练编码器-解码器模型及大型自回归语言模型的转变趋势。此外，我们深入探讨了评估摘要系统所面临的挑战，以及指令调优模型在零样本摘要任务中的潜力。最后，我们简要总结了当前摘要系统在商业应用中的整合方式。