@inproceedings{kumar-ekbal-2026-breaking,
title = "Breaking Bored? {S}ci{M}eme{X}: Towards Automatic Generation of Scientific Memes from Research Articles",
author = "Kumar, Sandeep and
Ekbal, Asif",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2264/",
pages = "51481--51504",
ISBN = "979-8-89176-459-0",
abstract = "The rapid growth of scientific literature makes it increasingly difficult to communicate a paper{'}s core novelty in a concise and accessible manner. While prior work has explored visual summaries such as graphical abstracts, emerging informal formats such as scientific memes remain largely unstudied from a computational perspective. Scientific memes rely on concise visual{--}textual juxtaposition and contrast to highlight how new work differs from prior approaches, making them a challenging test case for controlled abstraction, contrastive reasoning, and creative compression in generative models. We formalize scientific meme generation as a novel multimodal, contrastive generation task and propose SciMemeX, a modular multi-agent framework that extracts core innovations, selects contextually appropriate meme templates, and iteratively generates and refines captions through contrastive feedback-guided refinement. We evaluate generated memes along three complementary dimensions{---}\textit{scientific fidelity}, \textit{clarity}, and \textit{engagement potential}{---}and conduct extensive automatic and human evaluations on 1.5k computer science papers. Our results show that SciMemeX consistently outperforms strong single-agent prompting and existing multi-agent baselines across all evaluation dimensions, suggesting that scientific meme generation provides a constrained setting for exploring controlled creative generation in scientific communication context. We make the code public."
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<abstract>The rapid growth of scientific literature makes it increasingly difficult to communicate a paper’s core novelty in a concise and accessible manner. While prior work has explored visual summaries such as graphical abstracts, emerging informal formats such as scientific memes remain largely unstudied from a computational perspective. Scientific memes rely on concise visual–textual juxtaposition and contrast to highlight how new work differs from prior approaches, making them a challenging test case for controlled abstraction, contrastive reasoning, and creative compression in generative models. We formalize scientific meme generation as a novel multimodal, contrastive generation task and propose SciMemeX, a modular multi-agent framework that extracts core innovations, selects contextually appropriate meme templates, and iteratively generates and refines captions through contrastive feedback-guided refinement. We evaluate generated memes along three complementary dimensions—scientific fidelity, clarity, and engagement potential—and conduct extensive automatic and human evaluations on 1.5k computer science papers. Our results show that SciMemeX consistently outperforms strong single-agent prompting and existing multi-agent baselines across all evaluation dimensions, suggesting that scientific meme generation provides a constrained setting for exploring controlled creative generation in scientific communication context. We make the code public.</abstract>
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%0 Conference Proceedings
%T Breaking Bored? SciMemeX: Towards Automatic Generation of Scientific Memes from Research Articles
%A Kumar, Sandeep
%A Ekbal, Asif
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kumar-ekbal-2026-breaking
%X The rapid growth of scientific literature makes it increasingly difficult to communicate a paper’s core novelty in a concise and accessible manner. While prior work has explored visual summaries such as graphical abstracts, emerging informal formats such as scientific memes remain largely unstudied from a computational perspective. Scientific memes rely on concise visual–textual juxtaposition and contrast to highlight how new work differs from prior approaches, making them a challenging test case for controlled abstraction, contrastive reasoning, and creative compression in generative models. We formalize scientific meme generation as a novel multimodal, contrastive generation task and propose SciMemeX, a modular multi-agent framework that extracts core innovations, selects contextually appropriate meme templates, and iteratively generates and refines captions through contrastive feedback-guided refinement. We evaluate generated memes along three complementary dimensions—scientific fidelity, clarity, and engagement potential—and conduct extensive automatic and human evaluations on 1.5k computer science papers. Our results show that SciMemeX consistently outperforms strong single-agent prompting and existing multi-agent baselines across all evaluation dimensions, suggesting that scientific meme generation provides a constrained setting for exploring controlled creative generation in scientific communication context. We make the code public.
%U https://aclanthology.org/2026.emnlp-main.2264/
%P 51481-51504
Markdown (Informal)
[Breaking Bored? SciMemeX: Towards Automatic Generation of Scientific Memes from Research Articles](https://aclanthology.org/2026.emnlp-main.2264/) (Kumar & Ekbal, EMNLP 2026)
ACL