  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
