Abstract. When writing an academic paper, researchers often spend considerable time reviewing and summarizing papers to extract relevant citations and data to compose the Introduction and Related Work sections. To address this problem, we propose QuOTeS, an interactive system designed to retrieve sentences related to a summary of the research from a collection of potential references and hence assist in the composition of new papers. QuOTeS integrates techniques from Query-Focused Extractive Summarization and High-Recall Information Retrieval to provide Interactive Query-Focused Summarization of scientific documents. To measure the performance of our system, we carried out a comprehensive user study where participants uploaded papers related to their research and evaluated the system in terms of its usability and the quality of the summaries it produces. The results show that QuOTeS provides a positive user experience and consistently provides query-focused summaries that are relevant, concise, and complete. We share the code of our system and the novel Query-Focused Summarization dataset collected during our experiments at https://github.com/jarobyte91/quotes.

介绍了QuOTeS，一种交互式系统，它集成了Query-Focused Extractive Summarization和High-Recall Information Retrieval技术，旨在从潜在的引用文献集合中检索与研究摘要相关的句子，帮助撰写新论文，同时经过全面的用户研究表明，该系统能够提供积极的用户体验和相关的、简明、完整的摘要。

基于查询的技术摘要： QuOTeS