Challenges in Domain-Specific Abstractive Summarization and How to Overcome them
It tackles challenges in domain-specific abstractive summarization for researchers and practitioners, but is incremental as it focuses on assessing existing methods rather than introducing new ones.
This paper identifies three key limitations of large language models in domain-specific abstractive summarization—quadratic complexity, model hallucination, and domain shift—and assesses existing state-of-the-art techniques to address these research gaps.
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.