Frequently Asked Questions (FAQs) refer to the most common inquiries about specific content. They serve as content comprehension aids by simplifying topics and enhancing understanding through succinct presentation of information. In this paper, we address FAQ generation as a well-defined Natural Language Processing task through the development of an end-to-end system leveraging text-to-text transformation models. We present a literature review covering traditional question-answering systems, highlighting their limitations when applied directly to the FAQ generation task. We propose a system capable of building FAQs from textual content tailored to specific domains, enhancing their accuracy and relevance. We utilise self-curated algorithms to obtain an optimal representation of information to be provided as input and also to rank the question-answer pairs to maximise human comprehension. Qualitative human evaluation showcases the generated FAQs as well-constructed and readable while also utilising domain-specific constructs to highlight domain-based nuances and jargon in the original content.

翻译：常见问题解答（FAQ）指针对特定内容的常见询问。它们通过简化主题并以简洁的信息呈现方式增强理解，从而作为内容理解辅助工具。本文通过开发基于文本到文本转换模型的端到端系统，将FAQ生成定义为明确的自然语言处理任务。我们综述了传统问答系统的文献，并强调了其直接应用于FAQ生成任务时的局限性。我们提出了一种能够从特定领域文本内容中构建FAQ的系统，以提升其准确性和相关性。采用自研算法优化输入信息表示，并对问答对进行排序以最大化人类理解效率。定性人工评估表明，生成的FAQ结构清晰、可读性强，同时利用领域特定结构突出原始内容中的领域细微差别与专业术语。