  Due to the rapid pace of research publications, keeping up to date with all
the latest related papers is very time-consuming, even with daily feed tools.
There is a need for automatically generated, short, customized literature
reviews of sets of papers to help researchers decide what to read. While
several works in the last decade have addressed the task of explaining a single
research paper, usually in the context of another paper citing it, the
relationship among multiple papers has been ignored; prior works have focused
on generating a single citation sentence in isolation, without addressing the
expository and transition sentences needed to connect multiple papers in a
coherent story. In this work, we explore a feature-based, LLM-prompting
approach to generate richer citation texts, as well as generating multiple
citations at once to capture the complex relationships among research papers.
We perform an expert evaluation to investigate the impact of our proposed
features on the quality of the generated paragraphs and find a strong
correlation between human preference and integrative writing style, suggesting
that humans prefer high-level, abstract citations, with transition sentences
between them to provide an overall story.
