It is widely accepted that so-called facts can be checked by searching for
information on the Internet. This process requires a fact-checker to formulate
a search query based on the fact and to present it to a search engine. Then,
relevant and believable passages need to be identified in the search results
before a decision is made. This process is carried out by sub-editors at many
news and media organisations on a daily basis. Here, we ask the question as to
whether it is possible to automate the first step, that of query generation.
Can we automatically formulate search queries based on factual statements which
are similar to those formulated by human experts? Here, we consider similarity
both in terms of textual similarity and with respect to relevant documents
being returned by a search engine. First, we introduce a moderate-sized
evidence collection dataset which includes 390 factual statements together with
associated human-generated search queries and search results. Then, we
investigate generating queries using a number of rule-based and automatic text
generation methods based on pre-trained large language models (LLMs). We show
that these methods have different merits and propose a hybrid approach which
has superior performance in practice.

本文探讨了自动化生成基于文本相似度的查询，介绍了数据集和基于预训练大型语言模型的基于规则和自动化文本生成方法，并提出了一种优秀的混合方法。