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.

翻译：希望研究某一科学主题的学者必须投入时间阅读文献、提取意义并识别多篇论文之间的联系。随着科学文献数量的增长，这一过程变得愈发具有挑战性。与此同时，作者会在论文的"相关工作"章节中总结先前研究，但这种总结仅限于支持单一论文的范畴。一项形成性研究发现，虽然阅读多篇相关工作的段落有助于整体把握某个主题，但难以处理其中重叠或分叉的参考文献与研究焦点。在本研究中，我们设计了一个名为Relatedly的系统，为探索和阅读关于某一主题的多篇相关工作段落提供支架支持，其功能包括：动态重排序与高亮以聚焦未探索的差异信息、自动生成描述性段落标题、以及降低冗余信息的视觉权重。通过一项受试者内用户实验（n=15），我们发现，与基线论文列表相比，使用Relatedly的学者能够生成更具连贯性、洞察力和全面性的主题大纲。