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 (NLP) 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 our system capable of building FAQs from textual content tailored to specific domains, enhancing their accuracy and relevance. We utilise self-curated algorithms for obtaining optimal representation of information to be provided as input and also for ranking the question-answer pairs to maximise human comprehension. Qualitative human evaluation showcases the generated FAQs to be 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疑问集合。通过简化主题并以简洁方式呈现信息，FAQ有助于内容理解并提升认知效率。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生成定义为结构化的自然语言处理（NLP）任务。我们综述了传统问答系统的相关文献，揭示了其直接应用于FAQ生成任务时的局限性。随后提出一种能够从特定领域文本内容中构建FAQ的系统，显著提升了生成内容的准确性与相关性。该系统采用自研算法实现输入信息的优化表征，并通过对问答对进行排序以最大化人类理解效率。质性人工评估表明，生成的FAQ结构合理、可读性强，同时能够保留原始内容中的领域特异性表达与术语，凸显领域细粒度特征。