Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

Lundgard, Alan; Satyanarayan, Arvind

doi:10.1109/tvcg.2021.3114770

Cited by 65 publications

(105 citation statements)

References 80 publications

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“…However, each of these three approaches comes with its own limitations. Static alt text requires blind readers to accept the author's interpretation of the data; by not affording exploratory and interactive modes, alt text robs readers of the necessary time and space to interpret the numbers for themselves [LS21]. Recent research also suggests that blind people have nuanced preferences for the kinds of visual semantic content conveyed via text [PGFM21; LS21], and desire more interactive and exploratory representations of pictorial images [MJBC18].…”

Section: Background and Related Workmentioning

confidence: 99%

Rich Screen Reader Experiences for Accessible Data Visualization

Zong

Lee

Lundgard

et al. 2022

Computer Graphics Forum

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Current web accessibility guidelines ask visualization designers to support screen readers via basic non‐visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity—from fine‐grained datum‐by‐datum reading to skimming and surfacing high‐level trends. In response to the lack of comparable non‐visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co‐design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen‐reader‐accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed‐methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.

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Section: Background and Related Workmentioning

confidence: 99%

Rich Screen Reader Experiences for Accessible Data Visualization

Zong

Lee

Lundgard

et al. 2022

Computer Graphics Forum

Self Cite

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“…Most related to our work is a recent paper by Lundgard & Satyanarayan that began to formalize the semantic content of textual descriptions of charts [35]. They developed a conceptual model that spans four levels of semantic content: enumerating visualization construction properties (e.g., marks and encodings); reporting statistical concepts and relations (e.g., extrema and correlations); identifying perceptual and cognitive phenomena (e.g., complex trends and patterns); and elucidating domain-specific insights (e.g., social and political context).…”

Section: Accessibility (Blind and Low Vision)mentioning

confidence: 99%

“…In this study, we utilize the framework from Lundgard & Satyanarayan [35] which describes four semantic levels:…”

Section: Studymentioning

confidence: 99%

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Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

Stokes,

Setlur,

Cogley

et al. 2022

Preprint

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“…However, such alternative texts and captions are often not much helpful or not available at all [MJBC18]. A recent study found that chart description should explain important trends and key statistics rather than simply saying how the data is encoded [LS22]. In this context, introducing chart question answering could significantly advance the field of accessible data visualizations [SWM * 22].…”

Section: Challenges and Research Opportunitiesmentioning

confidence: 99%

Chart Question Answering: State of the Art and Future Directions

Hoque¹,

Kavehzadeh²,

Masry³

2022

Preprint

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Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights. Often people analyze charts to answer questions that they have in mind. Answering such questions can be challenging as they often require a significant amount of perceptual and cognitive effort. Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer to facilitate visual data analysis. Over the last few years, there has been a growing body of literature on the task of CQA. In this survey, we systematically review the current state-of-the-art research focusing on the problem of chart question answering. We provide a taxonomy by identifying several important dimensions of the problem domain including possible inputs and outputs of the task and discuss the advantages and limitations of proposed solutions. We then summarize various evaluation techniques used in the surveyed papers. Finally, we outline the open challenges and future research opportunities related to chart question answering. CCS Concepts• Human-centered computing → Visualization; • Computing methodologies → Natural language processing; Automatic chart question answering is a challenging task because of the richness and ambiguities of natural language and complex reasoning that may be required to predict the answer [SBT * 16]. The problem is also very inter-disciplinary in nature, falling in the intersection of information visualization, natural language processing, and human computer interactions. Some early approaches on NLIs and chart question answering applied natural language processing techniques by largely depending on heuristics or grammar-based parsing techniques [SBT * 16, SS18, HSTD17, GDA * 15]. Recently, there have been also some attempts for applying deep learning models for understanding natural language

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Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

Cited by 65 publications

References 80 publications

Rich Screen Reader Experiences for Accessible Data Visualization

Rich Screen Reader Experiences for Accessible Data Visualization

Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

Chart Question Answering: State of the Art and Future Directions

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