Efficient Extractive Summarization with MAMBA-Transformer Hybrids for Low-Resource Scenarios
Extractive summarization of long documents is bottlenecked by quadratic complexity, often forcing truncation and limiting deployment in resource-constrained settings. We introduce the first Mamba-Transformer hybrid for extractive summarization, combining the semantic strength of pre-trained transformers with the linear-time processing of state space models. Leveraging Mamba's ability to process full documents without truncation, our approach preserves context while maintaining strong summarization quality. The architecture includes: (1) a transformer encoder for sentence-level semantics, (2) a Mamba state space model to capture inter-sentence dependencies efficiently, and (3) a linear cla...
| Authors | Nisrine Ait Khayi |
|---|---|
| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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