@inproceedings{heinrich-etal-2026-illustrating,
title = "Illustrating Arguments with Images Using Aspect-Aware Prompting",
author = "Heinrich, Maximilian and
Anand, Sharat and
Kiesel, Johannes and
Stein, Benno",
editor = "Elaraby, Mohamed and
Hautli-Janisz, Annette and
Romberg, Julia and
Musi, Elena and
Ruggeri, Federico and
Lawrence, John",
booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.argmining-1.7/",
pages = "52--65",
ISBN = "979-8-89176-399-9",
abstract = "Images can powerfully strengthen arguments, conveying ideas more immediately and compellingly than text alone. With the rise of text-to-image models, a broad audience can now generate custom visuals to illustrate their arguments. Yet a fundamental mismatch undermines this potential: these models are trained on concrete scene descriptions, while arguments operate at the level of general, abstract principles. Naively prompting such a model with an argumentative text therefore rarely produces images that genuinely illustrate the argument. To address this challenge, we propose an aspect-aware image generation approach. Given an argument, our method first identifies the key aspects that an illustrative image should convey, then constructs a detailed scene description grounded in both the argument and those aspects, and finally generates an image using that scene description as the prompt. A human-assessment evaluation demonstrates that this approach yields images that illustrate arguments significantly better than those produced by naive prompting."
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<abstract>Images can powerfully strengthen arguments, conveying ideas more immediately and compellingly than text alone. With the rise of text-to-image models, a broad audience can now generate custom visuals to illustrate their arguments. Yet a fundamental mismatch undermines this potential: these models are trained on concrete scene descriptions, while arguments operate at the level of general, abstract principles. Naively prompting such a model with an argumentative text therefore rarely produces images that genuinely illustrate the argument. To address this challenge, we propose an aspect-aware image generation approach. Given an argument, our method first identifies the key aspects that an illustrative image should convey, then constructs a detailed scene description grounded in both the argument and those aspects, and finally generates an image using that scene description as the prompt. A human-assessment evaluation demonstrates that this approach yields images that illustrate arguments significantly better than those produced by naive prompting.</abstract>
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%0 Conference Proceedings
%T Illustrating Arguments with Images Using Aspect-Aware Prompting
%A Heinrich, Maximilian
%A Anand, Sharat
%A Kiesel, Johannes
%A Stein, Benno
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F heinrich-etal-2026-illustrating
%X Images can powerfully strengthen arguments, conveying ideas more immediately and compellingly than text alone. With the rise of text-to-image models, a broad audience can now generate custom visuals to illustrate their arguments. Yet a fundamental mismatch undermines this potential: these models are trained on concrete scene descriptions, while arguments operate at the level of general, abstract principles. Naively prompting such a model with an argumentative text therefore rarely produces images that genuinely illustrate the argument. To address this challenge, we propose an aspect-aware image generation approach. Given an argument, our method first identifies the key aspects that an illustrative image should convey, then constructs a detailed scene description grounded in both the argument and those aspects, and finally generates an image using that scene description as the prompt. A human-assessment evaluation demonstrates that this approach yields images that illustrate arguments significantly better than those produced by naive prompting.
%U https://aclanthology.org/2026.argmining-1.7/
%P 52-65
Markdown (Informal)
[Illustrating Arguments with Images Using Aspect-Aware Prompting](https://aclanthology.org/2026.argmining-1.7/) (Heinrich et al., ArgMining 2026)
ACL