VitaLITy: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics

TL;DR

VitaLITy is a system that leverages transformer models and visual analytics to facilitate serendipitous discovery of relevant academic literature, addressing terminology gaps in traditional review methods.

Contribution

The paper introduces VitaLITy, a novel system combining semantic search with visual analytics for literature review support, including new data and tools for community use.

Findings

Qualitative evaluation shows promising results for literature discovery.

VitaLITy visualizes semantic relationships in an interactive 2-D space.

Provides a new dataset from 38 visualization venues.

Abstract

There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VitaLITy, intended to complement existing practices. In particular, VitaLITy promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VitaLITy visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VitaLITy also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VitaLITy, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VitaLITy, and we provide scrapers for the open-source community to continue to grow the list of supported venues.