Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors. However, with a large corpus of papers, it's cognitively demanding to pairwise compare and contrast them all with each other. Fully automating this review process would be infeasible, because it often requires domain-specific knowledge, as well as understanding what the context and motivations for the review are. While there are existing tools to help with the process of organizing and annotating papers for literature reviews, at the core they still rely on people to serially read through papers and manually make sense of relevant information.
We present AVTALER, which combines peoples' unique skills, contextual awareness, and knowledge, together with the strength of automation. Given a set of comparable text excerpts from a paper corpus, it supports users in sensemaking and contrasting paper attributes by interactively aligning text excerpts in a table so that comparable details are presented in a shared column. AVTALER is based on a core alignment algorithm that makes use of modern NLP tools. Furthermore, AVTALER is a mixed-initiative system: users can interactively give the system constraints which are integrated into the alignment construction process.
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
Tallinn University（塔林大学）；School of Digital Technologies, Tallinn University（塔林大学数字技术学院）；Baltic Film, Media and Arts School, Tallinn University（塔林大学波罗的海电影、媒体与艺术学院）；School of Humanities, Tallinn University（塔林大学人文学院）
针对多维数据相关视觉数字工件集合探索分析的挑战，本文提出开源的Collection Space Navigator（CSN）交互式可视化工具，结合二维投影与多维过滤器，支持缩放、投影转换等操作，助力用户探索、整理集合并反馈见解。
