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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 sucharticles as an alternative to presenting a ranking of search results. Whilegenerative Large Language Models (LLMs) like chatGPT also address this task,they are known to hallucinate new information, their models are secret, hard toanalyze and control. Some generative LLMs provide supporting references, yetthese are often unrelated to the generated content. As an alternative, wepropose to study article generation systems that integrate document retrieval,query-specific clustering, and summarization. By design, such models canprovide actual citations as provenance for their generated text. In particular,we contribute an evaluation framework that allows to separately trains andevaluate each of these three components before combining them into one system.We experimentally demonstrate that a system comprised of the best-performingindividual components also obtains the best F-1 overall system quality.

Retrieve-Cluster-Summarize: An Alternative to End-to-End Training for Query-specific Article Generation