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.

通过开发一种利用文本到文本转换模型的端到端系统，本文研究了常见问题解答（FAQ）的生成作为一项明确定义的自然语言处理（NLP）任务，并且通过对传统问答系统的文献综述提出了我们的系统，能够根据特定领域的文本内容构建相关的 FAQ，提高准确性和相关性。我们利用自定义算法获取信息的最佳表示形式并对问题 - 答案对进行排名，以提高人类理解能力。定性人体评估显示生成的 FAQ 构思良好，易读，并利用特定领域的构造来突出原始内容中的领域差异和行业术语。

FAQ-Gen: 自动生成特定领域的常见问题解答以帮助理解内容

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

Existing conversational models are handled by a database(DB) and API based
systems. However, very often users' questions require information that cannot
be handled by such systems. Nonetheless, answers to these questions are
available in the form of customer reviews and FAQs. DSTC-11 proposes a three
stage pipeline consisting of knowledge seeking turn detection, knowledge
selection and response generation to create a conversational model grounded on
this subjective knowledge. In this paper, we focus on improving the knowledge
selection module to enhance the overall system performance. In particular, we
propose entity retrieval methods which result in an accurate and faster
knowledge search. Our proposed Named Entity Recognition (NER) based entity
retrieval method results in 7X faster search compared to the baseline model.
Additionally, we also explore a potential keyword extraction method which can
improve the accuracy of knowledge selection. Preliminary results show a 4 \%
improvement in exact match score on knowledge selection task. The code is
available this https URL

本文提出了一个基于客户评论和 FAQ 的交互式对话模型，主要通过围绕知识检索、知识选择和响应生成的三个阶段进行改进。我们主要针对知识选择模块的改进以提高整个系统的性能，我们提出了实体检索方法，其中基于命名实体识别的实体检索方法的搜索速度比基准模型提高了 7 倍。除此之外，我们还探索了一个关键词提取方法，该方法可以提高知识选择的准确性，初步结果显示知识选择任务的准确匹配得分提升了 4%。