An Interactive UI to Support Sensemaking over Collections of Parallel Texts
This addresses the challenge for scientists and science journalists who need to compare papers efficiently, though it is incremental as it builds on existing tools by adding interactive alignment.
The researchers tackled the problem of cognitively demanding pairwise comparison of large paper corpora by developing AVTALER, an interactive UI that aligns text excerpts in a table to support sensemaking, resulting in a mixed-initiative system that integrates user constraints with an NLP-based alignment algorithm.
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.