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.

翻译：摘要：在撰写学术论文时，研究人员通常需要花费大量时间阅读和总结文献，以提取相关引用和数据来撰写引言及相关工作部分。为解决该问题，我们提出了QuOTeS——一个交互式系统，旨在从潜在参考文献集合中检索与某研究总结相关的语句，从而辅助新论文的撰写。QuOTeS融合了查询聚焦式抽取型摘要生成技术和高召回率信息检索技术，为科学文档提供交互式查询聚焦摘要。为评估系统性能，我们开展了一项全面的用户研究：参与者上传与其研究相关的论文，并从系统可用性及其生成摘要的质量两个维度进行评价。结果表明，QuOTeS能提供良好的用户体验，并持续生成相关、简洁且完整的查询聚焦摘要。我们在https://github.com/jarobyte91/quotes 共享了系统代码及实验过程中收集的新型查询聚焦摘要数据集。