Visually-Summarized Pages (VS-Pages)

• VS-Pages are visual summaries that aggregate representative visual and semantic elements from multi-page, multimodal documents to facilitate efficient retrieval and understanding.
• They leverage document layout analysis, hybrid vector representations, and intentional relationship mining to achieve up to 99.82% reduction in computational overhead while maintaining high retrieval accuracy.
• Applications span legal discovery, scientific literature, enterprise knowledge management, and media summarization, offering scalable insights and streamlined user interfaces.

Visually-Summarized Pages (VS-Pages) are structures that aggregate and present visual and textual information from multi-page, multimodal documents, web sites, videos, or scientific articles to facilitate efficient retrieval, summarization, and sense-making. The concept has emerged as a focal point in web search, document understanding, video analysis, and scientific discovery, providing a principled approach for assembling representative visual layouts, condensing content, and serving as an interface for both coarse and fine-grained retrieval. VS-Pages integrate document layout analysis, hybrid vector representations, intentional relationship mining, and advanced summarization models to achieve high efficiency and accuracy in retrieval and summarization tasks.

1. Construction of VS-Pages

VS-Pages are constructed by aggregating representative visual and semantic elements from multiple source pages or document panels. Within frameworks such as HEAVEN (Kim et al., 25 Oct 2025), this involves:

• Document Layout Analysis (DLA): Using a model (e.g., DocLayout-YOLO) to extract key title layouts and other prominent regions from each page: $T_{k,i} = DLA(P_{k,i})$ for page $i$ in document $k$.
• Aggregation: All detected layouts for a document are unified, $T_k = \bigcup_{i=1}^{|D_k|} T_{k,i}$.
• Partition and Assembly: Layouts are divided into groups of $r$ consecutive pages, and each group is assembled into a VS-Page, $VS_k^{(j)} = Assemble(T_k^{(j)})$.
• Mapping: Each VS-Page is associated back to its constituent source pages via $$\Gamma(VS_k^{(j)}) = \{ P_{k,i} : T_{k,i} \cap T_k^{(j)} \neq \emptyset \}$$.

This process is typically performed offline at indexing time, resulting in a reduced set of VS-Pages $|VS| \ll |P|$ for downstream retrieval and summarization.

2. Hybrid Vector Retrieval and Efficiency

The HEAVEN framework (Kim et al., 25 Oct 2025) utilizes VS-Pages in a two-stage hybrid retrieval paradigm:

• Stage 1: Coarse retrieval operates over the compressed VS-Page set, leveraging single-vector similarity for efficient candidate selection. This reduces the computational burden by a factor of $r$.
• Stage 2: Fine reranking applies multi-vector methods only to candidates mapped from VS-Pages, focusing computation on linguistically important query tokens.

Efficiency gains are substantiated by reported figures: HEAVEN achieves a $99.82\%$ reduction in per-query FLOPs compared to state-of-the-art multi-vector techniques, with Recall@1 performance at $99.87\%$ of multi-vector models. This demonstrates that VS-Pages act as effective surrogates for coarse filtering without sacrificing downstream retrieval accuracy.

Framework	Efficiency Gain	Recall@1 (relative)
HEAVEN (VS-Pages)	99.82% fewer FLOPs	99.87% of SOTA
Multi-Vector Only	Baseline	100%

3. Application Domains and Functional Roles

VS-Pages are deployed across a spectrum of application domains:

• Legal discovery: Aggregating key layout regions from hundreds of visually rich legal documents enables high-speed retrieval and focused review.
• Scientific literature search: Summarization of figures, tables, and title layouts from research papers facilitates granular query answering and rapid triage.
• Enterprise knowledge management: VS-Pages encapsulate salient visual entities for efficient search and knowledge extraction in enterprise archives.
• Web and video summarization: Visual segments, keyframes, and entity summaries are organized into VS-Pages to succinctly present evolving or multi-modal content (Weng et al., 2022, Sahoo et al., 2017).

Their functional roles include representing summary candidate spaces, supporting iterative evidence gathering (as in SimpleDoc (Jain et al., 16 Jun 2025)), and providing interfaces for user-navigation and information retrieval.

4. Methodological Foundations

VS-Pages leverage several foundational technical approaches across different research:

• Document Layout Analysis: Core to VS-Page construction for visually rich documents, enabling extraction of title layouts and semantic segmentation (Kim et al., 25 Oct 2025).
• Hybrid Vector Representation: Combining the efficiency of single-vector retrieval over VS-Pages with the accuracy of multi-vector reranking (Kim et al., 25 Oct 2025).
• Submodular Summarization Models: Used for extracting diverse and representative visual entities, keyframes, or snippets from videos and image collections (Iyer et al., 2018, Sahoo et al., 2017).
• Intentional Relationship Mining: VS-Pages can surface SurfRel, SeekRel, and FactRel relationships by visualizing link flow or co-citation clusters among web pages to support information-gathering workflows (0810.5428).
• Self-Supervised Selection: Visual summaries (e.g., central figures in scientific articles) are assembled based on heuristic text–figure correspondence, scalable without human supervision (Yamamoto et al., 2020).

5. Comparative Analysis with Traditional Retrieval

Traditional retrieval approaches deal with each page (or token) independently, suffering from either excessive computation (multi-vector) or loss of granularity (single-vector). VS-Pages strike a balance:

• Compared to single-vector methods, VS-Pages retain access to structural and semantic richness by aggregating multiple informative regions for evaluation.
• Compared to exhaustive multi-vector methods, VS-Pages limit the candidate space, enabling computation to be focused on high-value regions identified during the layout analysis step.

A plausible implication is that hybrid VS-Page frameworks could serve as the foundation for future hybrid retrieval-augmented generation systems, further refining the interplay between layout, semantics, and linguistic importance.

6. Limitations and Future Research Directions

While VS-Pages deliver substantial efficiency and retrieval quality gains, several limitations are noted:

• Dependency on Document Layout Quality: The effectiveness of VS-Pages is bounded by the reliability of layout detection; noisy or unconventional documents may result in poor summarization.
• Selection of Representative Layouts: Choices about which regions to include (titles, figures, tables) can introduce bias or omit critical fields.
• Integration Challenges: Mapping VS-Page outputs to fine-grained downstream tasks (e.g., evidence-based QA, domain-specific summarization) may require customized partitioning or dynamic weighting mechanisms.

Research directions include tighter integration of semantic analysis with layout detection, further abstraction layers for hierarchical summarization, and adaptation of VS-Page concepts to multi-modal and multilingual corpora.

7. Impact for Information-Gathering and Summarization

VS-Pages enhance the utility of document retrieval and summarization pipelines by:

• Allowing systems to tailor recommendations or visual groupings based on user intent and information-gathering context (0810.5428).
• Enabling summaries that are not just contextually similar but also visually and structurally optimized for the end-user’s query.
• Supporting scalable operations over long, multi-modal documents, reducing user cognitive load while maximizing access to relevant information.

This comprehensive synthesis of hybrid vector retrieval, layout-based aggregation, and context-aware summarization establishes VS-Pages as a principal architectural component in modern systems for document search, discovery, and summarization, spanning legal, scientific, enterprise, and media domains.