Efficient Extractive Summarization with MAMBA-Transformer Hybrids for Low-Resource Scenarios

Nisrine Ait Khayi

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arXiv · 2603.01288

Efficient Extractive Summarization with MAMBA-Transformer Hybrids for Low-Resource Scenarios

Nisrine Ait Khayi

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Abstract

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 classifier for sentence relevance prediction. Across news, argumentative, and scientific domains under low-resource conditions, our method achieves: (1) large gains over BERTSUM and MATCHSUM, including +0.23 ROUGE-1 on ArXiv and statistically significant improvements on all datasets (p < 0.001); (2) consistent advantages across domains, strongest on the longest documents; (3) robust performance with limited training data; and (4) 24-27% faster inference on news summarization (CNN/DailyMail). We introduce the first hybrid Transformer-state space architecture for summarization, showing significant ROUGE improvements in low-resource scenarios.

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Nisrine Ait Khayi. 2026-03-01. Efficient Extractive Summarization with MAMBA-Transformer Hybrids for Low-Resource Scenarios. https://arxiv.org/abs/2603.01288

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