Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

EmphasisChecker: A Tool for Guiding Chart and Caption Emphasis (2307.13858v2)

Published 25 Jul 2023 in cs.HC

Abstract: Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EmphasisChecker, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (81)
  1. Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading. IEEE Transactions on Visualization and Computer Graphics, 25(1):661–671, 2019. doi: 10 . 1109/TVCG . 2018 . 2865119
  2. Beagle: Automated Extraction and Interpretation of Visualizations from the Web. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–8. ACM, New York, USA, 2018. doi: 10 . 1145/3173574 . 3174168
  3. F. Beck and D. Weiskopf. Word-Sized Graphics for Scientific Texts. IEEE Transactions on Visualization and Computer Graphics, 23(6):1576–1587, 2017. doi: 10 . 1109/TVCG . 2017 . 2674958
  4. A. D. Biderman. The Graph as a Victim of Adverse Discrimination and Segregation. Information Design Journal, 1(4):232–241, 1979. doi: 10 . 1075/idj . 1 . 4 . 03bid
  5. Beyond Memorability: Visualization Recognition and Recall. IEEE Transactions on Visualization and Computer Graphics, 22(1):519–528, 2016. doi: 10 . 1109/TVCG . 2015 . 2467732
  6. J. Brooke. SUS: A ’Quick and Dirty’ Usability Scale. In Usability Evaluation In Industry, chap. 21, pp. 189–194. Taylor & Francis Group, London, UK, 1996. doi: 10 . 1201/9781498710411-35
  7. Language Models Are Few-Shot Learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems. 159. Curran Associates Inc., Red Hook, USA, 2020. doi: 10 . 48550/arXiv . 2005 . 14165
  8. Information Graphics: An Untapped Resource for Digital Libraries. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 581–588. ACM, New York, USA, 2006. doi: 10 . 1145/1148170 . 1148270
  9. A. X. Chang and C. Manning. SUTime: A Library for Recognizing and Normalizing Time Expressions. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, pp. 3735–3740. ELRA, Paris, France, 2012.
  10. Neural Caption Generation over Figures. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 482–485. ACM, New York, USA, 2019. doi: 10 . 1145/3341162 . 3345601
  11. Figure Captioning with Reasoning and Sequence-Level Training. ArXiv, abs/1906.02850, 2019. doi: 10 . 48550/arXiv . 1906 . 02850
  12. D. Chen and C. Manning. A Fast and Accurate Dependency Parser Using Neural Networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 740–750. ACL, Stroudsburg, USA, 2014. doi: 10 . 3115/v1/D14-1082
  13. Z. Chen and H. Xia. CrossData: Leveraging Text-Data Connections for Authoring Data Documents. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 95. ACM, New York, USA, 2022. doi: 10 . 1145/3491102 . 3517485
  14. The Shape Parameter of a Two-Variable Graph. Journal of the American Statistical Association, 83(402):289–300, 1988. doi: 10 . 1080/01621459 . 1988 . 10478598
  15. DataSite: Proactive Visual Data Exploration with Computation of Insight-Based Recommendations. Information Visualization, 18(2):251–267, 2019. doi: 10 . 1177/1473871618806555
  16. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. ACL, Stroudsburg, USA, 2019. doi: 10 . 18653/v1/N19-1423
  17. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2):112–122, 1973. doi: 10 . 3138/FM57-6770-U75U-7727
  18. L. Earnest. The First Three Spelling Checkers. Retrieved July 18, 2023 from https://web.archive.org/web/20121022091418/http://www.stanford.edu/~learnest/spelling.pdf, 2011.
  19. Exploring and Exploiting the Limited Utility of Captions in Recognizing Intention in Information Graphics. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 223–230. ACL, Stroudsburg, USA, 2005. doi: 10 . 3115/1219840 . 1219868
  20. The Automated Understanding of Simple Bar Charts. Artificial Intelligence, 175(2):526–555, 2011. doi: 10 . 1016/j . artint . 2010 . 10 . 003
  21. M. Fasciano and G. Lapalme. PostGraphe: A System for the Generation of Statistical Graphics and Text. In Eighth International Natural Language Generation Workshop. ACL, Stroudsburg, USA, 1996.
  22. Incorporating Non-Local Information into Information Extraction Systems by Gibbs Sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 363–370. ACL, Stroudsburg, USA, 2005. doi: 10 . 3115/1219840 . 1219885
  23. TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text. IEEE Transactions on Visualization and Computer Graphics, 22(1):300–309, 2016. doi: 10 . 1109/TVCG . 2015 . 2467531
  24. Design Considerations for Enhancing Word-Scale Visualizations with Interaction. In Posters of the Conference on Information Visualization (InfoVis). IEEE, New York, USA, 2015.
  25. Google. Check Your Spelling & Grammar in Google Docs. Retrieved July 18, 2023 from https://support.google.com/docs/answer/57859?hl=en, 2023.
  26. Google. Fix Spelling & Grammar as You Type in Gmail. Retrieved July 18, 2023 from https://support.google.com/mail/answer/7987?hl=en, 2023.
  27. Google. Use Smart Compose. Retrieved July 18, 2023 from https://support.google.com/mail/answer/9116836?hl=en, 2023.
  28. Google Meet. Retrieved July 18, 2023 from https://meet.google.com/, 2023.
  29. J. D. Gould. Looking at Pictures. In R. A. Monty and J. W. Senders, eds., Eye Movements and Psychological Processes, chap. 5.2, pp. 323–345. Lawrence Erlbaum Associates, Inc., Hillsdale, USA, 1976.
  30. Grammarly. Retrieved July 18, 2023 from https://www.grammarly.com, 2023.
  31. Contextifier: Automatic Generation of Annotated Stock Visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2707–2716. ACM, New York, USA, 2013. doi: 10 . 1145/2470654 . 2481374
  32. International Monetary Fund. Retrieved July 18, 2023 from https://www.imf.org/en/Home, 2023.
  33. M4: A Visualization-Oriented Time Series Data Aggregation. Proceedings of the VLDB Endowment, 7(10):797–808, 2014. doi: 10 . 14778/2732951 . 2732953
  34. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems, 3(3):263–286, 2001. doi: 10 . 1007/PL00011669
  35. Benefits of Visualization in the Mammography Problem. International Journal of Human-Computer Studies, 83:94–113, 2015. doi: 10 . 1016/j . ijhcs . 2015 . 07 . 001
  36. Facilitating Document Reading by Linking Text and Tables. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pp. 423–434. ACM, New York, USA, 2018. doi: 10 . 1145/3242587 . 3242617
  37. Towards Understanding 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. 610. ACM, New York, USA, 2021. doi: 10 . 1145/3411764 . 3445443
  38. Frames and Slants in Titles of Visualizations on Controversial Topics. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12. ACM, New York, USA, 2018. doi: 10 . 1145/3173574 . 3174012
  39. 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, pp. 1–13. ACM, New York, USA, 2019. doi: 10 . 1145/3290605 . 3300576
  40. Extracting References between Text and Charts via Crowdsourcing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 31–40. ACM, New York, USA, 2014. doi: 10 . 1145/2556288 . 2557241
  41. Automatic Annotation Synchronizing with Textual Description for Visualization. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13. ACM, New York, USA, 2020. doi: 10 . 1145/3313831 . 3376443
  42. LanguageTool. Retrieved July 18, 2023 from https://languagetool.org/, 2023.
  43. Exploring Interactive Linking between Text and Visualization. In Proceedings of the 2018 Eurographics/IEEE VGTC Conference on Visualization: Short Papers, pp. 91–94. The EG Association, Eindhoven, Netherlands, 2018. doi: 10 . 2312/eurovisshort . 20181084
  44. Kori: Interactive Synthesis of Text and Charts in Data Documents. IEEE Transactions on Visualization and Computer Graphics, 28(1):184–194, 2022. doi: 10 . 1109/TVCG . 2021 . 3114802
  45. LibreOffice. Checking Spelling and Grammar. Retrieved July 18, 2023 from https://help.libreoffice.org/6.2/en-US/text/swriter/guide/spellcheck_dialog.html, 2023.
  46. VizByWiki: Mining Data Visualizations from the Web to Enrich News Articles. In Proceedings of the 2018 World Wide Web Conference, pp. 873–882. IW3C2, Geneva, Switzerland, 2018. doi: 10 . 1145/3178876 . 3186135
  47. A. Lundgard and A. Satyanarayan. Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content. IEEE Transactions on Visualization and Computer Graphics, 28(1):1073–1083, 2022. doi: 10 . 1109/TVCG . 2021 . 3114770
  48. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60. ACL, Stroudsburg, USA, 2014. doi: 10 . 3115/v1/P14-5010
  49. Merriam-Webster. Retrieved July 18, 2023 from https://www.merriam-webster.com, 2023.
  50. Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing. IEEE Transactions on Visualization and Computer Graphics, 18(12):2536–2545, 2012. doi: 10 . 1109/TVCG . 2012 . 199
  51. Microsoft. Editor Settings in Outlook.com and Outlook on the Web. Retrieved July 18, 2023 from https://support.microsoft.com/en-us/office/editor-settings-in-outlook-com-and-outlook-on-the-web-c6b1283d-81a2-47f1-bc85-9e8dfc0cbf15, 2023.
  52. R. Mitkov. Anaphora Resolution. Taylor & Francis Group, London, UK, 2014. doi: 10 . 4324/9781315840086
  53. J. Obeid and E. Hoque. Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model. In Proceedings of the 13th International Conference on Natural Language Generation, pp. 138–147. ACL, Stroudsburg, USA, 2020.
  54. The Curious Case of Combining Text and Visualization. In Proceedings of the 2019 Eurographics/IEEE VGTC Conference on Visualization: Short Papers. The EG Association, Eindhoven, Netherlands, 2019. doi: 10 . 2312/evs . 20191181
  55. Visually Communicating Bayesian Statistics to Laypersons. In Technical Report 2012-02. Tufts University, Medford, USA, 2012.
  56. Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability. IEEE Transactions on Visualization and Computer Graphics, 22(1):529–538, 2016. doi: 10 . 1109/TVCG . 2015 . 2467758
  57. Pew Research Center. Retrieved June 18, 2023 from https://www.pewresearch.org/, 2023.
  58. J. Pinheiro and J. Poco. ChartText: Linking Text with Charts in Documents. ArXiv, abs/2201.05043, 2022. doi: 10 . 48550/arXiv . 2201 . 05043
  59. J. Poco and J. Heer. Reverse-Engineering Visualizations: Recovering Visual Encodings from Chart Images. Computer Graphics Forum, 36(3):353–363, 2017. doi: 10 . 1111/cgf . 13193
  60. Generating Accurate Caption Units for Figure Captioning. In Proceedings of the Web Conference 2021, pp. 2792–2804. ACM, New York, USA, 2021. doi: 10 . 1145/3442381 . 3449923
  61. U. Ramer. An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing, 1(3):244–256, 1972. doi: 10 . 1016/S0146-664X(72)80017-0
  62. K. Rong and P. Bailis. ASAP: Prioritizing Attention via Time Series Smoothing. Proceedings of the VLDB Endowment, 10(11):1358–1369, 2017. doi: 10 . 14778/3137628 . 3137645
  63. P. Rosen and G. J. Quadri. LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts. IEEE Transactions on Visualization and Computer Graphics, 27(2):1536–1546, 2021. doi: 10 . 1109/TVCG . 2020 . 3030421
  64. Springer Nature. Retrieved July 18, 2023 from https://www.nature.com/, 2023.
  65. Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts. IEEE Transactions on Visualization and Computer Graphics, 29(1):1233–1243, 2023. doi: 10 . 1109/TVCG . 2022 . 3209383
  66. The Split-Attention Effect. In Cognitive Load Theory, chap. 9, pp. 111–128. Springer, New York, USA, 2011. doi: 10 . 1007/978-1-4419-8126-4_9
  67. Tableau Public. Retrieved July 18, 2023 from https://public.tableau.com/app/discover, 2023.
  68. Tableau Software. Retrieved July 18, 2023 from https://www.tableau.com, 2023.
  69. The British Broadcasting Corporation (BBC). Retrieved July 18, 2023 from https://www.bbc.com/, 2023.
  70. The New York Times. Retrieved July 18, 2023 from https://www.nytimes.com/, 2023.
  71. E. R. Tufte. The Visual Display of Quantitative Information. Graphics Press, Cheshire, USA, 2001.
  72. United Nations. International Labour Organization. Retrieved July 18, 2023 from https://www.ilo.org/global/lang--en/, 2023.
  73. U.S. Department of the Treasury. Retrieved July 18, 2023 from https://home.treasury.gov/, 2023.
  74. Vox Media. Retrieved July 18, 2023 from https://www.vox.com/, 2023.
  75. High School Girls’ Interpretations of Science Graphs: Exploring Complex Visual and Natural Language Hybrid Text. International Journal of Science and Mathematics Education, 14(8):1387–1406, 2016. doi: 10 . 1007/s10763-015-9677-7
  76. Wikimedia Commons. Retrieved July 18, 2023 from https://commons.wikimedia.org/wiki/Main_Page, 2023.
  77. G. Wills and L. Wilkinson. AutoVis: Automatic Visualization. Information Visualization, 9(1):47–69, 2010. doi: 10 . 1057/ivs . 2008 . 27
  78. WolframAlpha. Retrieved July 18, 2023 from https://www.wolframalpha.com/, 2023.
  79. The Curse of Knowledge in Visual Data Communication. IEEE Transactions on Visualization and Computer Graphics, 26(10):3051–3062, 2020. doi: 10 . 1109/TVCG . 2019 . 2917689
  80. How Do Captions Affect Visualization Reading? ArXiv, abs/2205.01263, 2022. doi: 10 . 48550/arXiv . 2205 . 01263
  81. Foresight: Rapid Data Exploration Through Guideposts. ArXiv, abs/1709.10513, 2017. doi: 10 . 48550/arXiv . 1709 . 10513

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now