Summary of Xl-headtags: Leveraging Multimodal Retrieval Augmentation For the Multilingual Generation Of News Headlines and Tags, by Faisal Tareque Shohan et al.
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags
by Faisal Tareque Shohan, Mir Tafseer Nayeem, Samsul Islam, Abu Ubaida Akash, Shafiq Joty
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to generating entity tags for news articles, which can help readers quickly identify relevant topics. The authors leverage auxiliary information like images and captions to improve content selection strategies and develop a dataset called XL-HeadTags containing 20 languages from six language families. By utilizing instruction tuning with variations, the model generates both headlines and tags in a multilingual context. The paper demonstrates the effectiveness of its multimodal-multilingual retrievers through extensive evaluation and contributes to the research community by providing tools for processing and evaluating multilingual texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a system that helps people find interesting articles online. They used extra information like pictures and captions to make it easier to choose important parts of long articles. This lets language models do a better job at suggesting headlines and topics. The team made a special dataset called XL-HeadTags with 20 languages from six different groups. They tested their system and showed that it works well. This will help scientists study how people read online news. |
Keywords
» Artificial intelligence » Instruction tuning
