# VisRecall: Quantifying Information Visualisation Recallability via Question Answering

Source: [VisRecall: Quantifying Information Visualisation Recallability via Question Answering](<https://www.sota2.com/research/paper/68fa6e7e1e209131cea404a4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:58:52.876Z

Authors: Yao Wang, Chuhan Jiao, Mihai B\\^ace, Andreas Bulling

Published: 2021-12-30T21:36:59.000Z

## Abstract

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.15217>)

[PDF](<https://arxiv.org/pdf/2112.15217.pdf>)