MMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware
MMLDSum-LLM enhances long-document summarization with visual alignment and keyword awareness, improving cross-modal consistency.
Key Findings
Methodology
MMLDSum-LLM uses a two-stage training framework combining supervised fine-tuning with visual-alignment weighted loss and keyword-aware weighted loss, followed by GRPO optimizing multi-objective rewards like keyword coverage, image-text alignment, ROUGE, and length control.
Key Results
- On MMLDSum-Bench, MMLDSum-LLM significantly improved keyword coverage and cross-modal consistency, achieving a GPT-4o overall score of 4.51.
- Achieved an atomic claim recall of 0.85, close to GPT-5's 0.90, outperforming other models.
- Two-stage training on Qwen3-VL-8B improved GPT-4o overall score by 5.1%.
Significance
This research is significant for academia and industry, addressing key information omission and cross-modal hallucination in multimodal long-document summarization, offering a more efficient knowledge transmission method.
Technical Contribution
MMLDSum-LLM's visual alignment and keyword-aware strategies significantly improve existing methods' shortcomings in long-sequence modeling and cross-modal alignment, offering new engineering possibilities.
Novelty
First to introduce visual alignment and keyword-aware strategies in multimodal long-document summarization, significantly improving information coverage and consistency compared to existing methods.
Limitations
- In extremely long documents, the model may still suffer from attention drift, leading to information omission.
- Requires substantial computational resources for training.
- Adaptability to specific domains needs further verification.
Future Work
Future work could include optimizing model performance on ultra-long documents, extending to more domains, or exploring more efficient training methods.
AI Executive Summary
Multimodal long documents are crucial for professional knowledge transmission, but existing multimodal models often miss key information or produce cross-modal hallucinations during summarization. MMLDSum-LLM introduces visual alignment and keyword-aware strategies, significantly enhancing cross-modal consistency and information coverage.
The method employs a two-stage training framework, starting with supervised fine-tuning with visual alignment and keyword-weighted loss, followed by GRPO optimizing multi-objective rewards. Experimental results show significant performance improvements on MMLDSum-Bench, especially in keyword coverage and cross-modal consistency.
However, challenges remain in handling extremely long documents. Future work will focus on further optimizing model performance and expanding application domains.
Deep Analysis
Background
Multimodal long documents like academic papers and medical reports play a crucial role in knowledge transmission, but their sparse and cross-modal information distribution makes traditional summarization methods ineffective. Existing multimodal summarization research often focuses on short texts or specific domains, lacking comprehensive support for long documents.
Core Problem
The core problem in multimodal long-document summarization is information omission and cross-modal hallucination, primarily due to attention drift in long-sequence dependency modeling and inadequate cross-modal alignment.
Innovation
MMLDSum-LLM enhances learning on key evidence through visual alignment and keyword-aware strategies, avoiding information omission and hallucination. Compared to traditional methods, it significantly improves information coverage and consistency.
Methodology
- �� Uses visual alignment weighting to enhance learning on image-associated spans.
- �� Keyword-aware weighting emphasizes TF-IDF-filtered key entities.
- �� GRPO optimizes multi-objective rewards, including keyword coverage and image-text alignment.
Experiments
Experiments were conducted on MMLDSum-Bench, covering multiple domains and context lengths, using a unified evaluation protocol including LLM-as-a-judge scoring, atomic claim precision/recall, image-text alignment, and ROUGE.
Results
MMLDSum-LLM significantly improved keyword coverage and cross-modal consistency, achieving a GPT-4o overall score of 4.51 and an atomic claim recall of 0.85, outperforming other models.
Applications
This method can be used for automatic summarization of multimodal long documents like academic papers and medical reports, improving information retrieval efficiency and reducing the burden of manual reading.
Limitations & Outlook
The model may still suffer from attention drift in extremely long documents, requires substantial computational resources for training, and its adaptability to specific domains needs further verification.
Plain Language Accessible to non-experts
Imagine you're in a large library searching for information. Each book has text and images, with information sparsely distributed. MMLDSum-LLM acts like a smart librarian, quickly finding and summarizing all important information. It identifies keywords and images to ensure no critical content is missed.
ELI14 Explained like you're 14
Imagine you're playing a game where the task is to find all important information from a pile of books. MMLDSum-LLM is like your super helper, quickly finding keywords and images, then giving you a concise summary! Isn't that cool?
Glossary
Visual Alignment
Enhances learning on image-associated spans to improve cross-modal consistency.
Used to improve accuracy in multimodal long-document summarization.
Keyword-Aware
Emphasizes TF-IDF-filtered key entities to enhance learning on key information.
Used to improve information coverage.
GRPO
A strategy for optimizing multi-objective rewards to ensure summary quality.
Used in the second stage of MMLDSum-LLM training.
ROUGE
A metric for evaluating summary quality by measuring n-gram overlap.
Used to assess coverage and quality of generated summaries.
Atomic Claim
Basic factual units in a summary used to evaluate accuracy and completeness.
Used to assess the precision of model-generated summaries.
Open Questions Unanswered questions from this research
- 1 How to further improve model attention stability on ultra-long documents?
- 2 How to enhance cross-domain adaptability?
- 3 Can the computational resource requirements be reduced?
Applications
Immediate Applications
Academic Paper Summarization
Helps researchers quickly access key information in papers, reducing reading time.
Medical Report Analysis
Assists doctors in quickly obtaining important information from patient reports, improving diagnostic efficiency.
Long-term Vision
Knowledge Management Systems
Enhances efficiency and accuracy in enterprise knowledge management through automatic summarization technology.
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.