  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.
