mlsanityData to 7 Oct 2026

XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags

arXiv 2406.03776 ↗First posted 6 Jun 2024cs.CL cs.AI cs.CVcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 50 of its 91 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

50 of 91 references.

References · 50

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38 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generationcited by 42
  2. 2026Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Modelscited by 5
  3. 2025OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviewscited by 3
  4. 2026Do Multimodal RAG Systems Leak Data? A Comprehensive Evaluation of Membership Inference and Image Caption Retrieval Attackscited by 3
  5. 2025eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tablescited by 2
  6. 2025COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generationcited by 2
  7. 2025Empowering Multimodal LLMs with External Tools: A Comprehensive Surveycited by 2
  8. 2026MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrievalcited by 2
  9. 2026Unified Vision-Language Modeling via Concept Space Alignmentcited by 0