Knowledge workers often need to extract and analyze information from a collection of documents to solve complex information tasks in the workplace, e.g., hiring managers reviewing resumes or analysts assessing risk in contracts. However, foraging for relevant information can become tedious and repetitive over many documents and criteria of interest. We introduce Marco, a mixed-initiative workspace supporting sensemaking over diverse business document collections. Through collection-centric assistance, Marco reduces the cognitive costs of extracting and structuring information, allowing users to prioritize comparative synthesis and decision making processes. Users interactively communicate their information needs to an AI assistant using natural language and compose schemas that provide an overview of a document collection. Findings from a usability study (n=16) demonstrate that when using Marco, users complete sensemaking tasks 16% more quickly, with less effort, and without diminishing accuracy. A design probe with seven domain experts identifies how Marco can benefit various real-world workflows.

翻译：知识工作者常需从文档集合中提取并分析信息，以完成工作场景中的复杂信息任务，例如招聘经理审阅简历或分析师评估合同风险。然而，针对大量文档和关注标准进行信息检索往往繁琐且重复。本文提出Marco——一个支持多种商业文档集合理解分析的混合主动工作空间。通过集合中心化辅助机制，Marco降低了信息提取与结构化的认知成本，使用户能够优先进行对比综合分析与决策制定。用户可通过自然语言与AI助手交互式沟通信息需求，并构建提供文档集合概览的架构图。可用性研究（n=16）结果表明，使用Marco时，用户完成理解分析任务的速度提升16%，所需努力更少且准确性不降低。一项针对七位领域专家的设计探索进一步揭示了Marco如何赋能真实场景中的各类工作流程。