Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summarization. This paper identifies three of those limitations as research problems in the context of abstractive text summarization: 1) Quadratic complexity of transformer-based models with respect to the input text length; 2) Model Hallucination, which is a model's ability to generate factually incorrect text; and 3) Domain Shift, which happens when the distribution of the model's training and test corpus is not the same. Along with a discussion of the open research questions, this paper also provides an assessment of existing state-of-the-art techniques relevant to domain-specific text summarization to address the research gaps.

翻译：大语言模型在通用数据及自然语言处理多项任务中表现出色，但在领域特定抽象文本摘要等任务中暴露出若干局限性。本文将其中的三个局限性确立为抽象文本摘要领域的研究问题：1）基于Transformer的模型在输入文本长度上存在的二次复杂度问题；2）模型幻觉现象，即模型生成事实错误文本的能力；3）领域偏移问题，即模型训练语料与测试语料分布不一致的情况。除探讨未决的研究问题外，本文还对现有与领域特定文本摘要相关的尖端技术进行了评估，以填补上述研究空白。