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EviProp method improves long document retrieval with graph diffusion

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EviProp, a novel method for retrieving relevant pages from long, visually rich documents. Unlike existing approaches that score pages independently, EviProp models documents as multimodal Chunk-Page graphs. It uses seeded relevance diffusion, combining query-page similarity with chunk-level signals to improve retrieval accuracy. Experiments on benchmark datasets show EviProp outperforms traditional methods and leads to better downstream question-answering performance. AI

IMPACT Enhances retrieval accuracy for complex multimodal documents, potentially improving AI systems that rely on document understanding.

RANK_REASON This is a research paper describing a new method for document retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EviProp method improves long document retrieval with graph diffusion

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This is a research paper describing a new method for document retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Guohang Yan ·

    EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval

    Retrieving evidence pages from visually rich long documents is a key challenge in document question answering. Existing page-level visual retrievers operate under an independent matching paradigm: each page is scored in isolation based on query-page similarity. This paradigm can …

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