# Upgrading the Newsroom: An Automated Image Selection System for News Articles

arXiv: https://arxiv.org/abs/2004.11449 · HTML: https://mlsanity.com/paper/2004.11449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [A Unified Understanding of Adversarial Vulnerability Regarding Unimodal Models and Vision-Language Pre-training Models](https://mlsanity.com/paper/2407.17797/) (2024)
- [Sample-agnostic Adversarial Perturbation for Vision-Language Pre-training Models](https://mlsanity.com/paper/2408.02980/) (2024)

## Abstract (arXiv)

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level word embeddings to help both modeling morphologically rich languages, e.g. German, and transferring knowledge across nearby languages. The text encoder adopts a hierarchical self-attention mechanism to attend more to both keywords within a piece of text and informative components of a news article. We extensively experiment with our system on a large-scale text-image database containing multimodal multilingual news articles collected from Swiss local news media websites. The system is compared with multiple baselines with ablation studies and is shown to beat existing text-image retrieval methods in a weakly-supervised learning setting. Besides, we also offer insights on the advantage of using multiple textual sources and multilingual data.