In an ever-expanding world of domain-specific knowledge, the increasing
complexity of consuming, and storing information necessitates the generation of
summaries from large information repositories. However, every persona of a
domain has different requirements of information and hence their summarization.
For example, in the healthcare domain, a persona-based (such as Doctor, Nurse,
Patient etc.) approach is imperative to deliver targeted medical information
efficiently. Persona-based summarization of domain-specific information by
humans is a high cognitive load task and is generally not preferred. The
summaries generated by two different humans have high variability and do not
scale in cost and subject matter expertise as domains and personas grow.
Further, AI-generated summaries using generic Large Language Models (LLMs) may
not necessarily offer satisfactory accuracy for different domains unless they
have been specifically trained on domain-specific data and can also be very
expensive to use in day-to-day operations. Our contribution in this paper is
two-fold: 1) We present an approach to efficiently fine-tune a domain-specific
small foundation LLM using a healthcare corpus and also show that we can
effectively evaluate the summarization quality using AI-based critiquing. 2) We
further show that AI-based critiquing has good concordance with Human-based
critiquing of the summaries. Hence, such AI-based pipelines to generate
domain-specific persona-based summaries can be easily scaled to other domains
such as legal, enterprise documents, education etc. in a very efficient and
cost-effective manner.

我们提出了一种使用医疗语料库有效微调领域特定小型基础语言模型 (Large Language Model) 的方法，并展示了可以使用基于人工智能的批评来有效评估摘要质量，并且这种基于人工智能的管道生成特定领域、针对个人需求的摘要可以以非常高效和低成本的方式轻松扩展到其他领域，如法律、企业文件、教育等。