A document clustering pipeline that builds a named-entity similarity graph, then runs graph convolutional clustering on LLM embeddings, outperforms co-occurrence and KNN graph baselines in the paper's experiments.
MLSUM: The Multilingual Summarization Corpus
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abstract
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
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Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering
A document clustering pipeline that builds a named-entity similarity graph, then runs graph convolutional clustering on LLM embeddings, outperforms co-occurrence and KNN graph baselines in the paper's experiments.