Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature

要旨

Reviewing the literature to understand relevant threads of past work is a critical part of research and vehicle for learning. However, as the scientific literature grows the challenges for users to find and make sense of the many different threads of research grow as well. Previous work has helped scholars to find and group papers with citation information or textual similarity using standalone tools or overview visualizations. Instead, in this work we explore a tool integrated into users' reading process that helps them with leveraging authors' existing summarization of threads, typically in introduction or related work sections, in order to situate their own work's contributions. To explore this we developed a prototype that supports efficient extraction and organization of threads along with supporting evidence as scientists read research articles. The system then recommends further relevant articles based on user-created threads. We evaluate the system in a lab study and find that it helps scientists to follow and curate research threads without breaking out of their flow of reading, collect relevant papers and clips, and discover interesting new articles to further grow threads.

著者
Hyeonsu B. Kang
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Joseph Chee Chang
Allen Institute for AI, Seattle, Washington, United States
Yongsung Kim
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Aniket Kittur
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
論文URL

https://doi.org/10.1145/3526113.3545660

会議: UIST 2022

The ACM Symposium on User Interface Software and Technology

セッション: Search and Exploration

6 件の発表
2022-11-02 23:30:00
2022-11-03 01:00:00