Challenges in Domain-Specific Abstractive Summarization and How to Overcome them

TL;DR

This paper discusses key challenges in domain-specific abstractive summarization using large language models, including complexity, hallucination, and domain shift, and reviews potential solutions and open research questions.

Contribution

It identifies critical limitations of current models in domain-specific summarization and assesses existing techniques to address these challenges, highlighting open research directions.

Findings

Transformer complexity grows quadratically with input length

Models often hallucinate factually incorrect information

Domain shift significantly impacts summarization accuracy

Abstract

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.