What is it about?
Low-resource languages often lack high-quality datasets for cross-lingual text summarization which makes it challenging to generate accurate summaries. ConVerSum is a novel approach that tackles this challenge by leveraging contrastive learning to generate summaries without requiring direct data for the target language.
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Why is it important?
Many languages have limited resources. This prevents advancements in natural language processing (NLP). Our approach enables cross-lingual summarization specifically for these languages that helps bridge the gap in cross-lingual NLP and makes information more accessible globally.
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This page is a summary of: ConVerSum: A Contrastive Learning-Based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents, ACM Transactions on Asian and Low-Resource Language Information Processing, April 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3722109.
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