@inproceedings{zhang-etal-2026-mmldsum,
title = "{MMLDS}um-{LLM}: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware",
author = "Zhang, Xianpeng and
Yang, Jiahua and
Chen, Dongyu and
Zhang, Lei and
Ma, Jian and
Xu, Guohuan and
Lu, Haonan and
Su, Tianhuang and
Wang, Chuangchuang and
Tang, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1987/",
pages = "42350--42370",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal long documents are core carriers of professional knowledge, where critical evidence is sparsely distributed across paragraphs and modalities. This easily causes key information omission and cross-modal hallucinations in summarization by multimodal LLMs. These issues stem from attention drift in long-range dependency modeling and gaps in inter-modal alignment. To address this, we introduce MMLDSum-Bench, a high-quality benchmark for multimodal long-document summarization, covering multiple domains, context-length scales, and visual-textual modality distributions. We further propose MMLDSum-LLM, a reproducible two-stage training framework that combines supervised fine-tuning with visual-alignment weighted loss and keyword-aware weighted loss, followed by GRPO with a multi-objective reward (keyword coverage, image-text alignment, ROUGE, and length control). Extensive experiments on MMLDSum-Bench, comparing against leading closed-source and open-source multimodal models under a unified evaluation protocol {---} including LLM-as-a-judge scoring, atomic-claim precision/recall, image-text alignment (ITA), and ROUGE {---} demonstrate that our approach significantly improves key-information coverage and cross-modal consistency."
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<abstract>Multimodal long documents are core carriers of professional knowledge, where critical evidence is sparsely distributed across paragraphs and modalities. This easily causes key information omission and cross-modal hallucinations in summarization by multimodal LLMs. These issues stem from attention drift in long-range dependency modeling and gaps in inter-modal alignment. To address this, we introduce MMLDSum-Bench, a high-quality benchmark for multimodal long-document summarization, covering multiple domains, context-length scales, and visual-textual modality distributions. We further propose MMLDSum-LLM, a reproducible two-stage training framework that combines supervised fine-tuning with visual-alignment weighted loss and keyword-aware weighted loss, followed by GRPO with a multi-objective reward (keyword coverage, image-text alignment, ROUGE, and length control). Extensive experiments on MMLDSum-Bench, comparing against leading closed-source and open-source multimodal models under a unified evaluation protocol — including LLM-as-a-judge scoring, atomic-claim precision/recall, image-text alignment (ITA), and ROUGE — demonstrate that our approach significantly improves key-information coverage and cross-modal consistency.</abstract>
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%0 Conference Proceedings
%T MMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware
%A Zhang, Xianpeng
%A Yang, Jiahua
%A Chen, Dongyu
%A Zhang, Lei
%A Ma, Jian
%A Xu, Guohuan
%A Lu, Haonan
%A Su, Tianhuang
%A Wang, Chuangchuang
%A Tang, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-mmldsum
%X Multimodal long documents are core carriers of professional knowledge, where critical evidence is sparsely distributed across paragraphs and modalities. This easily causes key information omission and cross-modal hallucinations in summarization by multimodal LLMs. These issues stem from attention drift in long-range dependency modeling and gaps in inter-modal alignment. To address this, we introduce MMLDSum-Bench, a high-quality benchmark for multimodal long-document summarization, covering multiple domains, context-length scales, and visual-textual modality distributions. We further propose MMLDSum-LLM, a reproducible two-stage training framework that combines supervised fine-tuning with visual-alignment weighted loss and keyword-aware weighted loss, followed by GRPO with a multi-objective reward (keyword coverage, image-text alignment, ROUGE, and length control). Extensive experiments on MMLDSum-Bench, comparing against leading closed-source and open-source multimodal models under a unified evaluation protocol — including LLM-as-a-judge scoring, atomic-claim precision/recall, image-text alignment (ITA), and ROUGE — demonstrate that our approach significantly improves key-information coverage and cross-modal consistency.
%U https://aclanthology.org/2026.findings-emnlp.1987/
%P 42350-42370
Markdown (Informal)
[MMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware](https://aclanthology.org/2026.findings-emnlp.1987/) (Zhang et al., Findings 2026)
ACL
- Xianpeng Zhang, Jiahua Yang, Dongyu Chen, Lei Zhang, Jian Ma, Guohuan Xu, Haonan Lu, Tianhuang Su, Chuangchuang Wang, and Kai Tang. 2026. MMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 42350–42370, Budapest, Hungary. Association for Computational Linguistics.