@inproceedings{abaskohi-etal-2025-cemtm,
title = "{CEMTM}: Contextual Embedding-based Multimodal Topic Modeling",
author = "Abaskohi, Amirhossein and
Li, Raymond and
Li, Chuyuan and
Joty, Shafiq and
Carenini, Giuseppe",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.590/",
pages = "11686--11703",
ISBN = "979-8-89176-332-6",
abstract = "We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images. CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference. A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities. Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and document-topic distributions. Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61. Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles."
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%0 Conference Proceedings
%T CEMTM: Contextual Embedding-based Multimodal Topic Modeling
%A Abaskohi, Amirhossein
%A Li, Raymond
%A Li, Chuyuan
%A Joty, Shafiq
%A Carenini, Giuseppe
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F abaskohi-etal-2025-cemtm
%X We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images. CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference. A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities. Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and document-topic distributions. Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61. Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles.
%U https://aclanthology.org/2025.emnlp-main.590/
%P 11686-11703
Markdown (Informal)
[CEMTM: Contextual Embedding-based Multimodal Topic Modeling](https://aclanthology.org/2025.emnlp-main.590/) (Abaskohi et al., EMNLP 2025)
ACL
- Amirhossein Abaskohi, Raymond Li, Chuyuan Li, Shafiq Joty, and Giuseppe Carenini. 2025. CEMTM: Contextual Embedding-based Multimodal Topic Modeling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 11686–11703, Suzhou, China. Association for Computational Linguistics.