With the explosive growth of textual information, summarization systems have become increasingly important. This work aims at indicating the current state of the art in abstractive text summarization concisely. 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。本研究旨在简明扼要地展示抽象式文本摘要领域的最新发展水平。为此，我们概述了当前向预训练编码器-解码器模型及大型自回归语言模型的范式转变。此外，我们深入探讨了摘要系统评估所面临的挑战，以及指令微调模型在零样本摘要任务中的潜力。最后，我们简要介绍了摘要系统当前在商业应用中的集成情况。