  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.
