SurveyAgent：一个用于个性化且高效研究综述的对话系统
SurveyAgent: A Conversational System for Personalized and Efficient Research Survey
AI总结：
本文提出对话系统 SurveyAgent，通过整合知识管理、文献推荐和查询问答三个模块，为研究人员提供贯穿文献综述全过程的个性化、高效支持。
摘要：
在人工智能等快速发展的研究领域中，管理并及时了解最新的科学文献已成为研究人员面临的一项重大挑战。尽管此前的研究已经利用人工智能来辅助文献检索、论文推荐和问答，但仍缺乏一个能够满足研究人员整体需求的综合性支持系统。本文介绍了 SurveyAgent，这是一种新颖的对话系统，旨在为研究人员提供个性化且高效的研究综述辅助。SurveyAgent 集成了三个关键模块：用于组织论文的 Knowledge Management（知识管理）模块、用于发现相关文献的 Recommendation（推荐）模块，以及用于更深入地参与内容互动的 Query Answering（查询问答）模块。该系统的突出之处在于提供了一个统一平台，可在文献综述过程的各个阶段为研究人员提供支持，并通过一个优先考虑用户交互和个性化的对话界面来实现这一点。我们的评估证明了 SurveyAgent 在简化研究活动方面的有效性，展示了其促进研究人员与科学文献互动方式的能力。
英文摘要：
In the rapidly advancing research fields such as AI, managing and staying abreast of the latest scientific literature has become a significant challenge for researchers. Although previous efforts have leveraged AI to assist with literature searches, paper recommendations, and question-answering, a comprehensive support system that addresses the holistic needs of researchers has been lacking. This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. This system stands out by offering a unified platform that supports researchers through various stages of their literature review process, facilitated by a conversational interface that prioritizes user interaction and personalization. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
