# A Joint Model for Multimodal Document Quality Assessment

arXiv: https://arxiv.org/abs/1901.01010 · HTML: https://mlsanity.com/paper/1901.01010/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction](https://mlsanity.com/paper/2308.07971/) (2023)
- [Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review](https://mlsanity.com/paper/2310.02235/) (2023)
- [Evaluating Deep Neural Networks for Image Document Enhancement](https://mlsanity.com/paper/2106.15286/) (2021)
- [Multimodal Classification: Current Landscape, Taxonomy and Future Directions](https://mlsanity.com/paper/2109.09020/) (2021)
- [A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia](https://mlsanity.com/paper/2410.19150/) (2024)

## Abstract (arXiv)

The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text --- such as images, font choices, and visual layout --- we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.