FAQ-Gen: An automated system to generate domain-specific FAQs to aid
  content comprehension
Sahil Kale Gautam Khaire Jay Patankar
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.