Topic pages aggregate useful information about an entity or concept into a single succinct and accessible article. Automated creation of topic pages would enable their rapid curation as information resources, providing an alternative to traditional web search. While most prior work has focused on generating topic pages about biographical entities, in this work, we develop a completely automated process to generate high-quality topic pages for scientific entities, with a focus on biomedical concepts. We release TOPICAL, a web app and associated open-source code, comprising a model pipeline combining retrieval, clustering, and prompting, that makes it easy for anyone to generate topic pages for a wide variety of biomedical entities on demand. In a human evaluation of 150 diverse topic pages generated using TOPICAL, we find that the vast majority were considered relevant, accurate, and coherent, with correct supporting citations. We make all code publicly available and host a free-to-use web app at: https://s2-topical.apps.allenai.org

翻译：主题页面将关于某个实体或概念的有用信息整合为一篇简洁易懂的文章。自动化创建主题页面能够快速将其整理为信息资源，为传统网络搜索提供替代方案。尽管此前研究主要集中于生成传记类实体的主题页面，本项工作则针对科学实体（特别是生物医学概念）开发了一套全自动化流程，可生成高质量主题页面。我们发布了TOPICAL——一个集检索、聚类与提示词驱动于一体的模型管线的Web应用程序及配套开源代码，使用户能按需轻松生成各类生物医学实体的主题页面。通过对150个由TOPICAL生成的多样化主题页面开展人工评估发现，绝大多数页面内容相关、准确且连贯，并附有正确的支撑引用。我们已公开全部代码，并托管了免费使用的Web应用程序，访问地址为：https://s2-topical.apps.allenai.org