XL-HeadTags：利用多模态检索增强实现多语言新闻标题与标签生成
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags
AI总结：
本文提出XL-HeadTags方法，利用图像和标题等多模态辅助信息检索相关句子，结合指令微调，在多语言环境下同时生成新闻标题和实体标签，并构建了覆盖20种语言的数据集及配套评估工具。
摘要：
每天在线发布的数百万篇新闻文章可能让读者应接不暇。标题和实体（主题）标签对于引导读者判断内容是否值得花时间阅读至关重要。虽然标题生成已被广泛研究，但标签生成在很大程度上仍未得到探索，然而它能为读者提供更好的途径来访问感兴趣的主题。在吸引读者注意力方面对简洁性的需求，要求改进内容选择策略，以识别长篇文章中显著且相关的片段，从而有效地引导语言模型。为解决这一问题，我们提出利用文章中嵌入的图像和标题等辅助信息来检索相关句子，并利用带变体的指令微调，在多语言语境下为新闻文章同时生成标题和标签。为了利用这些辅助信息，我们构建了一个名为XL-HeadTags的数据集，涵盖6个不同语系的20种语言。通过广泛评估，我们证明了所提出的即插即用多模态多语言检索器在这两项任务上的有效性。此外，我们还开发了一套用于处理和多语言文本评估的工具，通过实现跨语言更准确、更高效的分析，为研究社区做出了重要贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
