# Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

arXiv: https://arxiv.org/abs/2605.23910 · HTML: https://mlsanity.com/paper/2605.23910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 133 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 24 of 24)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!](https://mlsanity.com/paper/2010.06572/) (2020)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts](https://mlsanity.com/paper/2302.08958/) (2023)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning](https://mlsanity.com/paper/1510.03519/) (2015)
- [VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification](https://mlsanity.com/paper/2205.12029/) (2022)
- [UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning](https://mlsanity.com/paper/2305.09299/) (2023)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (2023)
- [Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation](https://mlsanity.com/paper/2004.12427/) (2020)
- [Multimodal Metadata Assignment for Cultural Heritage Artifacts](https://mlsanity.com/paper/2406.00423/) (2024)
- [From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification](https://mlsanity.com/paper/2408.00096/) (2024)
- [A deep learning approach for predicting the quality of online health expert question-answering services](https://mlsanity.com/paper/1612.07040/) (2016)
- [Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction](https://mlsanity.com/paper/1904.08499/) (2019)
- [A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches](https://mlsanity.com/paper/2111.02041/) (2021)
- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)
- [Sequence-aware multimodal page classification of Brazilian legal documents](https://mlsanity.com/paper/2207.00748/) (2022)
- [A Multi-Modal Multilingual Benchmark for Document Image Classification](https://mlsanity.com/paper/2310.16356/) (2023)
- [On-Device Document Classification using multimodal features](https://mlsanity.com/paper/2101.01880/) (2021)
- [Multimodal Side-Tuning for Document Classification](https://mlsanity.com/paper/2301.07502/) (2023)
- [Multimodal Classification of Teaching Activities from University Lecture Recordings](https://mlsanity.com/paper/2312.17262/) (2023)

## Built on by (most cited first, 1 of 1)

- [The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting](https://mlsanity.com/paper/2609.16267/) (2026)

## Abstract (arXiv)

Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (multiview). However, the field lacks a unified framework, a quantitative synthesis of its effectiveness, and clear guidance for practitioners. This systematic review addresses these gaps by analysing 139 primary studies. It introduces a formal framework to structure the field, presents the results of a qualitative analysis to identify key trends, and performs a random-effects meta-analysis (to our knowledge, the first focused on document classification) to quantify performance gains. Our meta-analysis reveals that multimodal fusion improves accuracy (mean gain of +5.28 percentage points, p=0.0016) significantly -- the F1-score effect is directionally positive but statistically non-significant in our primary model. Multiview fusion provides consistent but modest gains for accuracy (+4.67%), F1-score (+3.08%), and recall (all p<0.05). Critically, our qualitative synthesis uncovers challenges in reproducibility in methodological rigour: only 11.8% (multimodal) and 23.3% (multiview) of the studies use statistical tests to validate their findings, which undermines the reliability of many of their results. This review's primary contributions are a unifying framework, the first quantitative evidence base, and data-driven guidelines. This review concludes that successful information fusion depends not on algorithmic complexity, but on the strategic alignment of the fusion method with the task context and a commitment to more rigorous validation.