论文标题
升级新闻编辑室：新闻文章的自动图像选择系统
Upgrading the Newsroom: An Automated Image Selection System for News Articles
论文作者
论文摘要
我们提出了一个自动图像选择系统，以帮助照片编辑为新闻文章选择合适的图像。该系统融合了从新闻文章中提取的多个文本源，并接受多语言输入。它配备了Char-Level单词的嵌入，可以帮助建模形态上丰富的语言，例如德语，并跨附近语言转移知识。文本编码器采用了层次自我注意的机制，可以在新闻文章的一段文本和信息内容中更多地参加这两个关键字。我们在一个大规模的文本图像数据库上进行了广泛的实验，该数据库包含从瑞士本地新闻媒体网站收集的多模式多语言新闻文章。将该系统与具有消融研究的多个基线进行比较，并显示在弱监督的学习环境中可以击败现有的文本图像检索方法。此外，我们还提供有关使用多个文本源和多语言数据的优势的见解。
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.
