  Millions of news articles published online daily can overwhelm readers.
Headlines and entity (topic) tags are essential for guiding readers to decide
if the content is worth their time. While headline generation has been
extensively studied, tag generation remains largely unexplored, yet it offers
readers better access to topics of interest. The need for conciseness in
capturing readers' attention necessitates improved content selection strategies
for identifying salient and relevant segments within lengthy articles, thereby
guiding language models effectively. To address this, we propose to leverage
auxiliary information such as images and captions embedded in the articles to
retrieve relevant sentences and utilize instruction tuning with variations to
generate both headlines and tags for news articles in a multilingual context.
To make use of the auxiliary information, we have compiled a dataset named
XL-HeadTags, which includes 20 languages across 6 diverse language families.
Through extensive evaluation, we demonstrate the effectiveness of our
plug-and-play multimodal-multilingual retrievers for both tasks. Additionally,
we have developed a suite of tools for processing and evaluating multilingual
texts, significantly contributing to the research community by enabling more
accurate and efficient analysis across languages.
