Extractive summarization (ES) aims to generate a concise summary by selecting a subset of sentences from a document while maximizing relevance and minimizing redundancy. Although modern ES systems achieve high accuracy using powerful neural models, their deployment typically relies on CPU or GPU infrastructures that are energy-intensive and poorly suited for real-time inference in resource-constrained environments. In this work, we explore the feasibility of implementing McDonald-style extractive summarization on a low-power CMOS coupled oscillator-based Ising machine (COBI) that supports integer-valued, all-to-all spin couplings. We first propose a hardware-aware Ising formulation that reduces the scale imbalance between local fields and coupling terms, thereby improving robustness to coefficient quantization: this method can be applied to any problem formulation that requires k of n variables to be chosen. We then develop a complete ES pipeline including (i) stochastic rounding and iterative refinement to compensate for precision loss, and (ii) a decomposition strategy that partitions a large ES problem into smaller Ising subproblems that can be efficiently solved on COBI and later combined. Experimental results on the CNN/DailyMail dataset show that our pipeline can produce high-quality summaries using only integer-coupled Ising hardware with limited precision. COBI achieves 3-4.5x runtime speedups compared to a brute-force method, which is comparable to software Tabu search, and two to three orders of magnitude reductions in energy, while maintaining competitive summary quality. These results highlight the potential of deploying CMOS Ising solvers for real-time, low-energy text summarization on edge devices.

翻译：抽取式文本摘要旨在通过从文档中选择一个句子子集来生成简洁的摘要，同时最大化相关性和最小化冗余。尽管现代抽取式摘要系统利用强大的神经模型实现了高精度，但其部署通常依赖于CPU或GPU基础设施，这些设施能耗高且难以适应资源受限环境中的实时推理需求。本研究探讨了在支持整数值全连接自旋耦合的低功耗CMOS耦合振荡器伊辛机上实现McDonald式抽取式摘要的可行性。我们首先提出了一种硬件感知的伊辛问题表述方法，该方法通过减小局部场与耦合项之间的尺度不平衡，从而提升对系数量化的鲁棒性：此方法可应用于任何需要从n个变量中选择k个变量的问题表述。随后，我们开发了一套完整的抽取式摘要处理流程，包括：（i）采用随机舍入与迭代优化以补偿精度损失；（ii）设计分解策略，将大规模抽取式摘要问题划分为可在CMOS耦合振荡器伊辛机上高效求解的较小伊辛子问题，并最终合并结果。在CNN/DailyMail数据集上的实验结果表明，我们的流程仅需使用有限精度的整数耦合伊辛硬件即可生成高质量摘要。相较于暴力求解方法，CMOS耦合振荡器伊辛机实现了3至4.5倍的运行加速，其性能与软件禁忌搜索相当，同时能耗降低两到三个数量级，且保持了具有竞争力的摘要质量。这些结果凸显了在边缘设备上部署CMOS伊辛求解器以实现实时、低能耗文本摘要的潜力。