Passage Aware Contextual Encoding With Attention Masking

via “sequence-to-sequence-attention-mechanism-for-context-preservation”

summarization model by undefined. 1,76,763 downloads.

Unique: BART's multi-head cross-attention (12 heads, 16 layers) enables fine-grained tracking of which input spans influence each output token; unlike extractive models, attention is learned end-to-end rather than computed post-hoc, making it more semantically meaningful

vs others: More interpretable than black-box extractive summarizers and provides richer attention patterns than single-head attention mechanisms, enabling analysis of multiple attention strategies (e.g., some heads focus on recent context, others on long-range references)