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Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks — arXiv:2610.41360

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41360
License (as archived): CC0-1.0
Archived: 2026-11-03T05:45:35.790Z
SHA-256 of the archived text: 2ba1d8ecd649211f32c7af2f1952e01a4d85761f687ad7605a97ca041117dd22
Stable URL of this page: https://metraipolis.com/documento/dc_bcffd0c8aa02

How to cite

Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks — arXiv:2610.41360 — arXiv (en), https://arxiv.org/abs/2610.41360 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bcffd0c8aa02 (Metraipolis archive dc_bcffd0c8aa02, sha256 2ba1d8ecd649211f32c7af2f1952e01a4d85761f687ad7605a97ca041117dd22)

BibTeX

@misc{dcbcffd0c8aa02,
  title = {Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks — arXiv:2610.41360},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41360},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bcffd0c8aa02; sha256 2ba1d8ecd649211f32c7af2f1952e01a4d85761f687ad7605a97ca041117dd22},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41360. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In modern information retrieval (IR), documents are increasingly encountered as structured sequences of visually rich, paginated units, rather than as flat text. Example documents encountered in our daily life include Word files, PDFs, and slide decks. Free text-centric retrieval systems either ignore this structure entirely or destroy it through arbitrary passage chunking. In this paper, we formulate page-centric Visual Document Retrieval (VDR) with pages as the fundamental indexing units and document context available for assessing page relevance. The formulation distinguishes two retrieval granularities, page and document, and three retrieval paradigms: independent-item, evidence-set, and iterative retrieval. We then assess 42 benchmarks using eight criteria covering corpus characteristics, query construction, relevance annotation, and ranking support. Our analysis shows two main findings on current VDR research. First, evaluation remains concentrated on page-level independent-item retrieval, with fewer resources supporting document-level, evidence-set, and iterative settings. Second, queries originating from real user information needs independently of target corpus content and graded relevance judgments are uncommon, while the coverage and verification of relevance judgments vary substantially. Both findings reveal gaps between the capabilities applications require and those existing evaluation protocols can establish. We provide benchmark selection guidance and identify priorities for future benchmark design, emphasizing alignment between task requirements, relevance semantics, and evaluation protocols.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.IR, nuovi lavori del giorno).
Categorie arXiv: Information Retrieval (cs.IR) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.41360 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:33:19.063Z dalla pagina https://arxiv.org/list/cs.IR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).