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    <abstract>Online charts are ubiquitous, yet remain systematically inaccessible to screen reader users. This paper presents the process for developing the MATCHA Chart Summarizer (MCS), an interactive, browser-based system, leveraging multimodal LLM’s to generate accessible spoken chart summaries and support natural-language querying. MCS aims to enhance functional comprehension and narrow the reasoning gap for screen reader users when interacting with data visualizations. Through a three-stage mixed-methods, user-based evaluation (N = 126 total), we conduct a needs assessment, design our prototype, and demonstrate that LLM-generated summaries can improve task accuracy and increase user confidence, informativeness, and usability. We also quantify how visualization flaws, such as missing labels or poor contrast, induce semantic drift in generated chart summaries. We contribute an accessible assistive NLP system, an impact-oriented evaluation framework, and evidence-based design recommendations for automated chart summarization.</abstract>
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%0 Conference Proceedings
%T MATCHA: LLM-Based Chart Summarization for Screen Reader Users
%A Kahn, Lauren Claudia
%A Soto, Jaime
%A Arya, Om
%A Michal, Audrey
%A Wilson, Steven R.
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F kahn-etal-2026-matcha
%X Online charts are ubiquitous, yet remain systematically inaccessible to screen reader users. This paper presents the process for developing the MATCHA Chart Summarizer (MCS), an interactive, browser-based system, leveraging multimodal LLM’s to generate accessible spoken chart summaries and support natural-language querying. MCS aims to enhance functional comprehension and narrow the reasoning gap for screen reader users when interacting with data visualizations. Through a three-stage mixed-methods, user-based evaluation (N = 126 total), we conduct a needs assessment, design our prototype, and demonstrate that LLM-generated summaries can improve task accuracy and increase user confidence, informativeness, and usability. We also quantify how visualization flaws, such as missing labels or poor contrast, induce semantic drift in generated chart summaries. We contribute an accessible assistive NLP system, an impact-oriented evaluation framework, and evidence-based design recommendations for automated chart summarization.
%U https://aclanthology.org/2026.nlp4pi-1.24/
%P 269-282

Markdown (Informal)

[MATCHA: LLM-Based Chart Summarization for Screen Reader Users](https://aclanthology.org/2026.nlp4pi-1.24/) (Kahn et al., NLP4PI 2026)

• MATCHA: LLM-Based Chart Summarization for Screen Reader Users (Kahn et al., NLP4PI 2026)

ACL

• Lauren Claudia Kahn, Jaime Soto, Om Arya, Audrey Michal, and Steven R Wilson. 2026. MATCHA: LLM-Based Chart Summarization for Screen Reader Users. In Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI), pages 269–282, Budapest, Hungary. Association for Computational Linguistics.