  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.
