On The Persona-based Summarization of Domain-Specific Documents

TL;DR

This paper proposes a method to fine-tune small domain-specific language models for persona-based summarization of complex, domain-specific documents, demonstrating effective AI-based evaluation that aligns with human judgment, enabling scalable and cost-effective summaries.

Contribution

The paper introduces a fine-tuning approach for small domain-specific LLMs and validates AI-based critique as a reliable, scalable alternative to human evaluation for persona-based summarization.

Findings

Effective fine-tuning of small LLMs on healthcare data.

AI-based critique aligns well with human judgment.

Scalable, cost-effective summarization across domains.

Abstract

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.