# FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension

arXiv: https://arxiv.org/abs/2402.05812 · HTML: https://mlsanity.com/paper/2402.05812/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [A Survey on Transfer Learning in Natural Language Processing](https://mlsanity.com/paper/2007.04239/) (2020)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System](https://mlsanity.com/paper/2302.00907/) (2023)
- [On the Generation of Medical Question-Answer Pairs](https://mlsanity.com/paper/1811.00681/) (2018)
- [AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents](https://mlsanity.com/paper/2103.03820/) (2021)
- [Question Generation from Paragraphs: A Tale of Two Hierarchical Models](https://mlsanity.com/paper/1911.03407/) (2019)

## Built on by (most cited first, 2 of 2)

- [Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs](https://mlsanity.com/paper/2511.18931/) (2025)
- [ExpertGenQA: Open-ended QA generation in Specialized Domains](https://mlsanity.com/paper/2503.02948/) (2025)

## Abstract (arXiv)

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.