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

• URL: http://arxiv.org/abs/2307.00963v1
• Date: Mon, 3 Jul 2023 12:26:44 GMT
• Title: Challenges in Domain-Specific Abstractive Summarization and How to Overcome them
• Abstract summary: Large Language Models 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.
• Score: 0.6021787236982659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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