Multi-label text classification is important for organizing large document collections, but correlated labels and skewed label distributions make representation learning difficult. Existing multi-label contrastive methods usually define pairwise supervision from label overlap, resulting in a coarse signal limited to observed shared labels. We propose Greedy Matching Aggregation Soft Contrastive Learning (GMA-SCL), which constructs contrastive targets for MLTC by greedily matching label texts and incorporating label importance weighting. The resulting reference distribution is sharpened to emphasize highly similar candidate documents while preserving graded supervision from moderately similar ones. Experiments on AAPD, Reuters-21578, and UK-LEX show that GMA-SCL improves Macro-F1 over existing multi-label contrastive baselines while maintaining competitive Micro-F1 and Hamming Loss. Our code is available at https://github.com/koba-84/gma-scl.