2024
EMNLP
EMNLP 2024
Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach
Abstract
AbstractCross-lingual summarization aims to bridge language barriers by summarizing documents in different languages. However, ensuring semantic coherence across languages is an overlooked challenge and can be critical in several contexts. To fill this gap, we introduce multi-target cross-lingual summarization as the task of summarizing a document into multiple target languages while ensuring that the produced summaries are semantically similar. We propose a principled re-ranking approach to this problem and a multi-criteria evaluation protocol to assess semantic coherence across target languages, marking a first step that will hopefully stimulate further research on this problem.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— multi-target language
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Generation > Summarization
Natural Language Processing > Resources & Methods > Multilingual NLP
Natural Language Processing > Applications > Summarization
Machine Learning > Learning Types > Multi-Modal Learning