arXiv:2606.01252v1 Announce Type: new Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored. To address this gap, we introduce multi-target cross-lingual element-aware (MEA), a new MTXLS benchmark covering 24 target languages. We benchmark end-to-end and pipeline approaches across various LLMs and show that MTXLS performance still substantially lags behind English monolingual summarization. To better understand MTXLS in
The proliferation of LLMs and the increasing demand for global content consumption necessitates advancements in cross-lingual summarization capabilities.
This research highlights a significant performance gap in multi-target cross-lingual summarization, indicating current LLM limitations for truly global applications.
The explicit identification of performance disparities pushes research and development towards specialized solutions for multi-target, cross-lingual NLP tasks.
- · AI researchers
- · Multilingual content platforms
- · LLM developers focused on global deployment
- · Translation service providers
- · Generic LLM solutions for global summarization
Increased focus on developing new architectures and datasets specifically for multi-target cross-lingual summarization.
Improved tools for global communication and information dissemination, reducing language barriers in various sectors.
Accelerated adoption of AI in previously language-constrained markets, potentially leveling the playing field for non-English content creators.
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Read at arXiv cs.CL