  Query-specific article generation is the task of, given a search query,
generate a single article that gives an overview of the topic. We envision such
articles as an alternative to presenting a ranking of search results. While
generative Large Language Models (LLMs) like chatGPT also address this task,
they are known to hallucinate new information, their models are secret, hard to
analyze and control. Some generative LLMs provide supporting references, yet
these are often unrelated to the generated content. As an alternative, we
propose to study article generation systems that integrate document retrieval,
query-specific clustering, and summarization. By design, such models can
provide actual citations as provenance for their generated text. In particular,
we contribute an evaluation framework that allows to separately trains and
evaluate each of these three components before combining them into one system.
We experimentally demonstrate that a system comprised of the best-performing
individual components also obtains the best F-1 overall system quality.
