  Scholars who want to research a scientific topic must take time to read,
extract meaning, and identify connections across many papers. As scientific
literature grows, this becomes increasingly challenging. Meanwhile, authors
summarize prior research in papers' related work sections, though this is
scoped to support a single paper. A formative study found that while reading
multiple related work paragraphs helps overview a topic, it is hard to navigate
overlapping and diverging references and research foci. In this work, we design
a system, Relatedly, that scaffolds exploring and reading multiple related work
paragraphs on a topic, with features including dynamic re-ranking and
highlighting to spotlight unexplored dissimilar information, auto-generated
descriptive paragraph headings, and low-lighting of redundant information. From
a within-subjects user study (n=15), we found that scholars generate more
coherent, insightful, and comprehensive topic outlines using Relatedly compared
to a baseline paper list.
