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REVIEW 3 major objections 4 minor 72 references

Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Pluto is a prototype that treats a chart and its text as one authoring surface, with chart selections finishing sentences and drafted text re-sorting or annotating the chart.

desk verdict Useful systems paper with a genuine bidirectional authoring loop and an honest but thin evaluation; the central 'semantic alignment' claim is not directly measured. read the letter →

arxiv 2502.07725 v1 pith:KBLPZL2V submitted 2025-02-11 cs.HC

Arjun Srinivasan , Vidya Setlur , Arvind Satyanarayan This is my paper · ORCID
classification cs.HC
keywords data-drivencommunicationmixed-initiativeauthoringchartcaptioninglargelanguagemodelsvisualizationtext-chartalignmentannotationssemanticmodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Pluto is a mixed-initiative authoring prototype that tries to establish a simple but under-supported idea: the text that accompanies a chart and the chart itself should be authored together, with each modality shaping the other. The paper argues that chart-construction features (encodings, data, selections) and whatever text the user has already drafted are enough to generate useful recommendations — full descriptions, titles, sentence completions, verification flags, and chart changes such as sorting or highlighting. A user can brush a region of the chart and have the system finish a sentence, or type a description and have the system suggest a title, add annotations, and reorder the chart to match the narrative. The paper reports a preliminary study with ten participants suggesting the recommendations help bootstrap authoring and that different authors adopt different workflows, from generate-then-edit to writing manually and asking for suggestions. The contribution is the demonstration of a bidirectional text-chart authoring loop plus a schema and pipeline that make it work.

What carries the argument

The load-bearing object is the conceptual schema that unifies the two modalities. A chart is data mapped onto a chart specification grammar, plus an active selection and annotations; a description is a list of statements, each carrying text, one of five semantic statement types, and references to data items; titles and annotations similarly carry data items. The schema is what lets a parser extract data references and statement types from prose and saliency scores from the chart, and it is what the recommendation engine compares to decide whether to suggest text additions, verification flags, annotations, or sorting. Keeping text and chart in one structured form is the mechanism that makes the bidirectional flow possible.

What would settle it

A controlled blind comparison would settle the central claim: randomize authors to write chart-plus-text documents either in Pluto or in a plain chart editor with a text box, then score the documents on factual accuracy against the data and on text-chart cohesion without knowing which tool produced them. If Pluto documents are no more accurate or cohesive, the claim that the recommendations align text and charts is not supported. A cheaper check is to sample statements Pluto flags for verification and compare their error rate to unflagged statements.

Watch

Extended reading notes

Core claim

The central claim is that a tool can meaningfully support joint text-and-chart authoring when it keeps a structured representation of both modalities and passes that structure to a combination of heuristics and a large language model. Pluto models a chart as data mapped onto a specification with an active selection and annotations, and models text as a title plus a description made of statements, each tagged with a semantic type (encoding, perceptual-trend, data-fact, domain-specific, other) and the data items it references. On that basis the system recommends full text, sentence completions, statement additions or reorderings, statements that need manual verification, chart annotations, and sorting. The paper's own framing is that this is a proof-of-concept: the evaluation is preliminary, and the recommendations are suggestions under the author's control rather than automated outputs.

Load-bearing premise

The load-bearing premise is that ten participants from one company's mailing lists, each using Pluto once on three charts, provide enough evidence that the recommendations actually help joint authoring; the paper itself labels the evaluation preliminary.

Editorial extensions

If this is right

  • Authors can start from either side: generate text from the chart, or let drafted text drive chart changes such as sorting and highlighting.
  • Selections on the chart become part of the context for text generation, so sentence completions and callouts stay tied to the marks the author cares about.
  • Descriptions can be checked statement by statement against the chart data, so potentially incorrect trend or value claims can be flagged for human verification.
  • A prototype-scale study suggests authoring support is not one-size-fits-all: generate-then-edit, text-completion-guided, clipboard, and manual-draft strategies all appeared.
  • Because the schema is chart-type agnostic, the same recommendation modules can be pointed at other visual forms such as maps and adjacency matrices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, a direct quantitative test is available: have writers produce descriptions with and without Pluto-style verification flags and measure the rate of factual mismatches against the chart; the paper reports no such number.
  • If the bidirectional flow scales, the natural next step the paper leaves open is the reverse direction it does not implement: when the author edits the chart, the already-written sentences could be re-checked and revised, not just when text triggers chart changes.
  • The typed-statement schema could double as an accessibility lint: descriptions that lack encoding or perceptual-trend statements could be flagged as incomplete alt text, a use the paper sketches but does not implement.
  • The study's observed preference for selection-guided generation over full-text generation suggests future systems might prioritize scoped, on-demand suggestions over one-shot captions; this is an inference from participant quotes, not a measured result.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. Pluto is a mixed-initiative authoring system that supports jointly creating charts and accompanying text for data-driven communication. The system uses a conceptual schema capturing chart structure, selections, annotations, and text statements, then combines heuristics with GPT-4 to generate recommendations: full descriptions, titles, annotations, sentence completions, statement verification flags, and chart changes such as sorting or emphasis marks. The paper reports a preliminary qualitative study with ten participants, finding that recommendations were most useful for bootstrapping authoring and for chart design suggestions, and it derives design implications for future systems. The authors explicitly characterize the evaluation as preliminary and discuss limitations and future work.

Significance. If the system works as described, it explores a largely underexplored design space: bidirectional, mixed-initiative authoring in which text and charts mutually inform each other. The paper makes a useful contribution by operationalizing prior semantic models of chart text (Lundgard and Satyanarayan's four-level model) in a concrete system, and by providing a detailed architecture that combines chart parsing, text parsing, heuristics, and LLM generation. The reported qualitative observations, including the identification of distinct authoring strategies, provide a useful starting point for future tool design. Strengths include the detailed system description, the explicit grounding in prior semantic and mixed-initiative frameworks, and the candid discussion of the study's preliminary nature. The main weakness is that the evaluation does not directly measure the property named in the title: semantic alignment.

major comments (3)
  1. [Section 5.3, Figure 10] The study measures participants' self-reported helpfulness of the features, but it never evaluates the semantic alignment of the final text-chart artifacts. Thus the paper's central claim—that Pluto supports authoring "semantically aligned" text and charts—is not directly tested. A suggestion can be rated helpful while the resulting text remains misaligned with the data or the chart's emphasis. Please either add an evaluation of the produced artifacts using a rubric grounded in the four-level model of [34], or explicitly reframe the contribution as a tool that supports authoring workflows, with alignment as a design goal rather than an evaluated outcome.
  2. [Section 5.2] The study has no baseline condition and no objective measures of authoring outcomes (e.g., suggestion acceptance rates, time on task, or independent ratings of final text and chart quality). The authors justify the absence of a baseline, but the consequence is that the paper cannot support even a preliminary quantitative claim that Pluto improves alignment or text quality over unassisted authoring or over a generic LLM. At minimum, please report descriptive statistics on how often each recommendation type was accepted or led to edits, and relate these to the qualitative claims about utility.
  3. [Section 4.5, Figure 9] The annotation and sorting recommendations depend on "saliency scores" and a "combined saliency score," but the manuscript does not define how these scores are computed, what the thresholds are, or how the text-chart overlap is operationalized. Similarly, the statement verification step in Figure 8B checks whether a phrase like "significant fluctuation" can be confirmed, but the matching procedure is not specified. Without these details, the mechanism that supposedly produces semantic alignment is underspecified and not reproducible. Please provide the full scoring rules and verification algorithm, either in the text or in a supplementary appendix.
minor comments (4)
  1. [Throughout] There are several typos, including "preceptual-trend" for "perceptual-trend" (in Section 4.5 and Figure 8) and "pprovide" for "provide" in Section 7.
  2. [Section 4.4] The phrase "random forest classifier with BERT [42]" is ambiguous: it should clarify whether BERT embeddings are used as features or whether a BERT-based classifier is combined with random forests. Also, the cross-validation accuracy of the statement-type classifier on the 2147-statement dataset is not reported, despite the classifier driving several recommendations.
  3. [Section 4.1 and Figure 2] The inline icon glyphs (e.g., "/feather-altGenerate" and "♂lightbulbSuggest") may not render in all publication formats; please also provide textual button labels or a legend.
  4. [Section 7] The limitation statement mentions that the evaluation is only preliminary, but this is placed in the final section; consider acknowledging the construct-validity limitation (no direct alignment measure) also in Section 5.2 or 5.3, where the evaluation is presented.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Pluto's recommendations are system features computed from chart and text inputs, not derived predictions, and the study is an explicitly preliminary qualitative evaluation.

full rationale

This paper is a systems and qualitative-study paper, not a derivation of a scientific law, so most circularity patterns do not apply. The recommendation engine's outputs (full-text generation, sentence completion, statement verification, annotation suggestions, sorting suggestions) are deliberately defined as functions of the chart specification, the underlying data, and the user's text; for example, the sorting recommendation compares the order of data items mentioned in the description with the order in the chart and suggests a matching sort. That is a design decision, not a hidden reduction of a claimed prediction to its inputs, because the paper makes no empirical claim that these heuristics are validated as predictions. The user study in Section 5 measures self-reported helpfulness and observed usage strategies, and the paper itself states in Section 7 that 'this evaluation is only preliminary'; the gap between subjective ratings and actual semantic alignment is a construct-validity limitation, not a circularity. The one notable self-citation is the Lundgard and Satyanarayan four-level semantic model [34], which is used as the statement-type taxonomy, the classifier training data (Section 4.4), and a prompt constraint for description generation (Section 4.5). Although one Pluto author is also an author of that model, the model is previously published, is accompanied by a released dataset of 2147 chart description statements, and is used as a component rather than as an unverified premise that forces the paper's conclusions. No equation, fitted parameter, or derived result in the paper reduces to its own input by construction. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a systems paper, not a derivation. No free parameters or invented entities were introduced. The main assumptions are domain assumptions about the value of text-chart alignment, drawn from prior work, and the choice of GPT-4 as the LLM backend. No novel axioms are required.

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Cite this review

Pith. "Pith review of Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication." pith.science (2026). https://pith.science/paper/KBLPZL2V

@misc{pith2026250207725,
  author       = {Pith},
  title        = {Pith review of: Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KBLPZL2V}},
  note         = {Machine review of arXiv:2502.07725}
}
read the original abstract

Textual content (including titles, annotations, and captions) plays a central role in helping readers understand a visualization by emphasizing, contextualizing, or summarizing the depicted data. Yet, existing visualization tools provide limited support for jointly authoring the two modalities of text and visuals such that both convey semantically-rich information and are cohesively integrated. In response, we introduce Pluto, a mixed-initiative authoring system that uses features of a chart's construction (e.g., visual encodings) as well as any textual descriptions a user may have drafted to make suggestions about the content and presentation of the two modalities. For instance, a user can begin to type out a description and interactively brush a region of interest in the chart, and Pluto will generate a relevant auto-completion of the sentence. Similarly, based on a written description, Pluto may suggest lifting a sentence out as an annotation or the visualization's title, or may suggest applying a data transformation (e.g., sort) to better align the two modalities. A preliminary user study revealed that Pluto's recommendations were particularly useful for bootstrapping the authoring process and helped identify different strategies participants adopt when jointly authoring text and charts. Based on study feedback, we discuss design implications for integrating interactive verification features between charts and text, offering control over text verbosity and tone, and enhancing the bidirectional flow in unified text and chart authoring tools.

Figures

Figures reproduced from arXiv: 2502.07725 by the authors.

Figure 1
Figure 1. Examples of text and chart suggestions in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Pluto’s user interface. The key components include a data panel (A), chart editor (B), chart title (C), main chart canvas (D), and a chart description (E). Here, the user has manually entered a description and clicked the  Suggest button to get ideas on improving the chart and text for communication purposes. This results in the system suggesting a title and adding a highlight annotation for Single Family homes, wh… view at source ↗
Figure 3
Figure 3. Upon processing a description, Pluto flags state￾ments that require manual verification (A) and automatically annotates the chart based on data references in the descrip￾tion (B). Pluto also suggests a title, “Action and Animation Dominate: Gross Earnings by Genre (2010-2019)” based on both the narrative in the description and the underlying trends in the chart (Figure 1A￾top). Finally, besides suggestions for the t… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Examples of Pluto’s recommendations including an in-place sentence completion (A), annotations based on a chart’s description (B), and a text callout generated based on marks selected on a chart (C). Ronnie notices that most states, with the exception of Texas and New …
Figure 6
Figure 6. Figure 6: Pluto’s system architecture overview Note that this schema is not exhaustive (e.g., there may be ad￾ditional types of annotations, statement types, or chart selections) and was primarily designed to operationalize the recommendations in Pluto. However, we hope that the…
Figure 5
Figure 5. Figure 5: Conceptual schema representing the key text and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 7
Figure 7. Figure 7: Overview of full-text recommendation generation. Given the context of the chart, data, and any existing text, [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Overview of the description statement recommendations in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Summary of Pluto’s process for recommending chart design changes based on the authored text. Given a chart and accompanying text, the system extracts data references from both the chart and the text, and compares the references to suggest potential design changes to ma…
Figure 10
Figure 10. Figure 10: Participant responses to post-session questions [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Examples illustrating the extensibility afforded by the proposed conceptual schema. Here, [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]

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Works this paper leans on

72 extracted references · 53 canonical work pages

  1. [34]

    Alan Lundgard and Arvind Satyanarayan. 2021. Accessible Visualization via Natural Language Descriptions: A Four-level Model of Semantic Content. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 1073–1083

    work page 2021

  2. [1]

    Vega: A Visualization Grammar

    2014. Vega: A Visualization Grammar. https://vega.github.io/vega/. Accessed: January 2025

    work page 2014

  3. [2]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774 (2023)

    arXiv 2023

  4. [3]

    Md Zubair Ibne Alam, Shehnaz Islam, and Enamul Hoque. 2023. SeeChart: En- abling Accessible Visualizations Through Interactive Natural Language Interface for People with Visual Impairments. In Proceedings of the 28th International Conference on Intelligent User Interfaces . 46–64

    work page 2023

  5. [4]

    Robert Amar, James Eagan, and John Stasko. 2005. Low-Level Components of Analytic Activity in Information Visualization. InIEEE Symposium on Information Visualization, 2005. INFOVIS 2005. IEEE, 111–117

    work page 2005

  6. [5]

    Rahul C Basole and Timothy Major. 2024. Generative AI for Visualization: Opportunities and Challenges. IEEE Computer Graphics and Applications 44, 2 (2024), 55–64

    work page 2024

  7. [6]

    Leilani Battle, Peitong Duan, Zachery Miranda, Dana Mukusheva, Remco Chang, and Michael Stonebraker. 2018. Beagle: Automated Extraction and Interpretation of Visualizations from the Web. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems . 1–8

    work page 2018

  8. [7]

    Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (Oct 2001), 5–32. https://doi.org/10.1023/A:1010933404324 Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication IUI ’25, March 24–27, 2025, Cagliari, Italy

    doi:10.1023/a:1010933404324 2001

Show all 72 references
  1. [8]

    Zhutian Chen and Haijun Xia. 2022. Crossdata: Leveraging Text-Data Connec- tions for Authoring Data Documents. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems . 1–15

    2022

  2. [9]

    Jiwon Choi and Jaemin Jo. 2022. Intentable: A Mixed-initiative System for Intent- based Chart Captioning. In 2022 IEEE Visualization and Visual Analytics (VIS) . IEEE, 40–44

    2022

  3. [10]

    Matthew Conlen, Megan Vo, Alan Tan, and Jeffrey Heer. 2021. Idyll Studio: A Structured Editor for Authoring Interactive & Data-Driven Articles. In The 34th Annual ACM Symposium on User Interface Software and Technology . 1–12

    2021

  4. [11]

    Corinna Cortes and Vladimir Naumovich Vapnik. 1995. Support-Vector Networks. Machine Learning 20 (1995), 273–297. https://api.semanticscholar.org/CorpusID: 52874011

    1995

  5. [12]

    Zhe Cui, Sriram Karthik Badam, M Adil Yalçin, and Niklas Elmqvist. 2019. Dat- asite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations. Information Visualization 18, 2 (2019), 251–267

    2019

  6. [13]

    Haas, Srinivasan Parthasarathy, and Tejaswini Pedap- ati

    Çağatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, and Tejaswini Pedap- ati. 2017. Foresight: Recommending Visual Insights. Proc. VLDB Endow. 10, 12 (Aug. 2017), 1937–1940. https://doi.org/10.14778/3137765.3137813

    2017

  7. [14]

    Duda and Peter E

    Richard O. Duda and Peter E. Hart. 1974. Pattern Classification and Scene Analysis. In A Wiley-Interscience Publication. https://api.semanticscholar.org/CorpusID: 12946615

    1974

  8. [15]

    Maceachren, and Ross Ma- ciejewski

    Arlen Fan, Fan Lei, Michelle Mancenido, Alan M. Maceachren, and Ross Ma- ciejewski. 2024. Understanding Reader Takeaways in Thematic Maps Under Varying Text, Detail, and Spatial Autocorrelation. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (H...

    2024

  9. [16]

    Tong Gao, Mira Dontcheva, Eytan Adar, Zhicheng Liu, and Karrie G Karahalios

  10. [17]

    Yi He, Shixiong Cao, Yang Shi, Qing Chen, Ke Xu, and Nan Cao. 2024. Leveraging Large Models for Crafting Narrative Visualization: A Survey. arXiv preprint arXiv:2401.14010 (2024)

    2024 arXiv

  11. [18]

    Eric Horvitz. 1999. Principles of Mixed-Initiative User Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Pittsburgh, Pennsylvania, USA) (CHI ’99). Association for Computing Machinery, New York, NY, USA, 159–166. https://doi.org/10.1145...

    1999

  12. [19]

    Ting-Yao Hsu, C Lee Giles, and Ting-Hao Huang. 2021. SciCap: Generating Captions for Scientific Figures. In Findings of the Association for Computational Linguistics: EMNLP 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for ...

    2021 doi

  13. [20]

    Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, C Lee Giles, and Ting-Hao Kenneth Huang. 2024. SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings. In Extended Abstracts of the 2024 ...

    2024

  14. [21]

    Crescentia Jung, Shubham Mehta, Atharva Kulkarni, Yuhang Zhao, and Yea- Seul Kim. 2021. Communicating Visualizations Without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments. IEEE Transactions on Visualization and Computer Graphics 28...

    2021

  15. [22]

    Dae Hyun Kim, Seulgi Choi, Juho Kim, Vidya Setlur, and Maneesh Agrawala

  16. [23]

    Dae Hyun Kim, Vidya Setlur, and Maneesh Agrawala. 2021. Towards Under- standing How Readers Integrate Charts and Captions: A Case Study with Line Charts. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–11

    2021

  17. [24]

    Explain What a Treemap is

    Gyeongri Kim, Jiho Kim, and Yea-Seul Kim. 2023. “Explain What a Treemap is”: Exploratory Investigation of Strategies for Explaining Unfamiliar Chart to Blind and Low Vision Users. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–13

    2023

  18. [25]

    Hyunwoo Kim, Khanh Duy Le, Gionnieve Lim, Dae Hyun Kim, Yoo Jin Hong, and Juho Kim. 2024. DataDive: Supporting Readers’ Contextualization of Statis- tical Statements with Data Exploration. In Proceedings of the 29th International Conference on Intelligent User Interfaces . 623–639

    2024

  19. [26]

    Ha-Kyung Kong, Zhicheng Liu, and Karrie Karahalios. 2018. Frames and Slants in Titles of Visualizations on Controversial Topics. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems . 1–12

    2018

  20. [27]

    Ha-Kyung Kong, Zhicheng Liu, and Karrie Karahalios. 2019. Trust and Recall of Information Across Varying Degrees of Title-visualization Misalignment. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . 1–13

    2019

  21. [28]

    Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, and Sheelagh Carpendale. 2012. Empirical Studies in Information Visualization: Seven Sce- narios. IEEE Transactions on Visualization and Computer Graphics 18, 9 (2012), 1520–1536. https://doi.org/10.1109/TVCG.2011.279

    2012 doi

  22. [29]

    Shahid Latif, Zheng Zhou, Yoon Kim, Fabian Beck, and Nam Wook Kim. 2021. Kori: Interactive Synthesis of Text and Charts in Data Documents. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 184–194

    2021

  23. [30]

    Yanna Lin, Haotian Li, Leni Yang, Aoyu Wu, and Huamin Qu. 2023. Inksight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  24. [31]

    Can Liu, Yuhan Guo, and Xiaoru Yuan. 2023. AutoTitle: An Interactive Title Generator for Visualizations. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  25. [32]

    Can Liu, Liwenhan Xie, Yun Han, Datong Wei, and Xiaoru Yuan. 2020. AutoCap- tion: An Approach to Generate Natural Language Description from Visualization Automatically. In 2020 IEEE Pacific Visualization Symposium (PacificVis) . IEEE, 191–195

    2020

  26. [33]

    Annie Louis and Ani Nenkova. 2014. Verbose, Laconic or Just Right: A Simple Computational Model of Content Appropriateness under Length Constraints. 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014. https://doi.org/10.311...

    2014 doi

  27. [35]

    Manelski and Gilbert K

    Denis M. Manelski and Gilbert K. Krulee. 1965. A Heuristic Approach to Natural Language Processing. In COLING 1965. https://aclanthology.org/C65-1018

    1965

  28. [36]

    Arpit Narechania, Arjun Srinivasan, and John Stasko. 2020. NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 369–379

    2020

  29. [37]

    Jason Obeid and Enamul Hoque. 2020. Chart-to-Text: Generating Natural Lan- guage Descriptions for Charts by Adapting the Transformer Model. In Interna- tional Conference on Natural Language Generation . https://api.semanticscholar. org/CorpusID:224704949

    2020

  30. [38]

    Alvitta Ottley, Aleksandra Kaszowska, R Jordan Crouser, and Evan M Peck. 2019. The Curious Case of Combining Text and Visualization.EuroVis 2019-Short Papers (2019)

    2019

  31. [39]

    Joanna Purich, Arjun Srinivasan, Michael Correll, Leilani Battle, Vidya Setlur, and Anamaria Crisan. 2023. Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public. arXiv preprint arXiv:2306.16513 (2023)

    2023 arXiv

  32. [40]

    Ghulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno, Paul Rosen, and Danielle Albers Szafir. 2024. Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension. In Proceedings of the 2024 CHI Conference on Human Factors in Computi...

    2024

  33. [41]

    Bahador Saket, Alex Endert, and Çağatay Demiralp. 2018. Task-Based Effective- ness of Basic Visualizations. IEEE Transactions on Visualization and Computer Graphics 25, 7 (2018), 2505–2512

    2018

  34. [42]

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distil- BERT, A Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter. arXiv preprint arXiv:1910.01108 (2019)

    2019 arXiv

  35. [43]

    Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, and Danyel Fisher

  36. [44]

    Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer

  37. [45]

    Hans-Jörg Schulz, Thomas Nocke, Magnus Heitzler, and Heidrun Schumann

  38. [46]

    Edward Segel and Jeffrey Heer. 2010. Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics 16, 6 (2010), 1139–1148

    2010

  39. [47]

    Vidya Setlur, Sarah E Battersby, Melanie Tory, Rich Gossweiler, and Angel X Chang. 2016. Eviza: A Natural Language Interface for Visual Analysis. In Pro- ceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST). 365–377

    2016

  40. [48]

    Leixian Shen, Haotian Li, Yun Wang, Tianqi Luo, Yuyu Luo, and Huamin Qu

  41. [49]

    Ben Shneiderman. 2003. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In The Craft of Information Visualization . Elsevier, 364–371

    2003

  42. [50]

    Ben Shneiderman and Catherine Plaisant. 2006. Strategies for Evaluating In- formation Visualization Tools: Multi-Dimensional In-Depth Long-Term Case Studies. In Proceedings of the 2006 A VI Workshop on BEyond Time and Errors: Novel IUI ’25, March 24–27, 2025, Cagliari, Italy S...

    2006

  43. [51]

    Nikhil Singh, Lucy Lu Wang, and Jonathan Bragg. 2024. FigurA11y: AI Assistance for Writing Scientific Alt Text. In Proceedings of the 29th International Conference on Intelligent User Interfaces . 886–906

    2024

  44. [52]

    Arjun Srinivasan, Steven M Drucker, Alex Endert, and John Stasko. 2018. Aug- menting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication. IEEE Transactions on Visualization and Computer Graphics 25, 1 (2018), 672–681

    2018

  45. [53]

    Arjun Srinivasan and Vidya Setlur. 2021. Snowy: Recommending Utterances for Conversational Visual Analysis. In The 34th Annual ACM Symposium on User Interface Software and Technology (UIST) (Virtual Event, USA) (UIST ’21). Association for Computing Machinery, New York, NY, USA...

    2021

  46. [54]

    Chase Stokes, Cindy Xiong Bearfield, and Marti A Hearst. 2023. The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  47. [55]

    Chase Stokes and Marti A Hearst. 2021. Give Text A Chance: Advocating for Equal Consideration for Language and Visualization. In NL VIZ: Workshop on Exploring Opportunities and Challenges for Natural Language Techniques to Support Visual Analysis. IEEE

    2021

  48. [56]

    Chase Stokes, Vidya Setlur, Bridget Cogley, Arvind Satyanarayan, and Marti A Hearst. 2022. Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 1233–1243

    2022

  49. [57]

    Chris Stolte, Diane Tang, and Pat Hanrahan. 2008. Polaris: A System for Query, Analysis, and Visualization of Multidimensional Databases. Commun. ACM 51, 11 (Nov 2008), 75–84. https://doi.org/10.1145/1400214.1400234

    2008

  50. [58]

    Nicole Sultanum, Fanny Chevalier, Zoya Bylinskii, and Zhicheng Liu. 2021. Lever- aging Text-Chart Links to Support Authoring of Data-Driven Articles with VizFlow. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan)(CHI ’21). Associ...

    2021

  51. [59]

    Nicole Sultanum and Arjun Srinivasan. 2023. Datatales: Investigating the Use of Large Language Models for Authoring Data-driven Articles. In 2023 IEEE Visualization and Visual Analytics (VIS) . IEEE, 231–235

    2023

  52. [60]

    Benny J Tang, Angie Boggust, and Arvind Satyanarayan. 2023. Vistext: A Bench- mark for Semantically Rich Chart Captioning. arXiv preprint arXiv:2307.05356 (2023)

    2023 arXiv

  53. [61]

    Walker and David B

    Strother H. Walker and David B. Duncan. 1967. Estimation of the Probability of an Event as a Function of Several Independent Variables. Biometrika 54, 1/2 (1967), 167–179. http://www.jstor.org/stable/2333860

    1967

  54. [62]

    Yun Wang, Zhitao Hou, Leixian Shen, Tongshuang Wu, Jiaqi Wang, He Huang, Haidong Zhang, and Dongmei Zhang. 2022. Towards Natural Language-based Vi- sualization Authoring. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 1222–1232

    2022

  55. [63]

    Yun Wang, Zhida Sun, Haidong Zhang, Weiwei Cui, Ke Xu, Xiaojuan Ma, and Dongmei Zhang. 2019. Datashot: Automatic Generation of Fact Sheets from Tabular Data. IEEE Transactions on Visualization and Computer Graphics 26, 1 (2019), 895–905

    2019

  56. [64]

    Zhibiao Wu and Martha Palmer. 1994. Verbs Semantics and Lexical Selection. In Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics (Las Cruces, New Mexico) (ACL ’94). Association for Computational Linguistics, USA, 133–138. https://doi.org/10.311...

    1994

  57. [65]

    Victoria Yaneva, Irina Temnikova, and Ruslan Mitkov. 2015. Accessible Texts for Autism: An Eye-Tracking Study. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility . 49–57

    2015

  58. [66]

    Li Yujian and Liu Bo. 2007. A Normalized Levenshtein Distance Metric. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 6 (2007), 1091–1095

    2007

  59. [2013]

    IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2366–2375

    A Design Space of Visualization Tasks. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2366–2375

    2013

  60. [2015]

    In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST)

    Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST). 489–500

  61. [2016]

    IEEE Transactions on Visualization and Computer Graphics 23, 1 (2016), 341–350

    Vega-lite: A Grammar of Interactive Graphics. IEEE Transactions on Visualization and Computer Graphics 23, 1 (2016), 341–350

    2016

  62. [2018]

    What Do We Talk About When We Talk About Dashboards? IEEE Transac- tions on Visualization and Computer Graphics 25, 1 (2018), 682–692

    2018

  63. [2023]

    IEEE Transactions on Visualization and Computer Graphics (2023)

    EmphasisChecker A Tool for Guiding Chart and Caption Emphasis. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  64. [2024]

    IEEE Transactions on Visualization and Computer Graphics (2024)

    Data Playwright: Authoring Data Videos with Annotated Narration. IEEE Transactions on Visualization and Computer Graphics (2024)

    2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.