Extractive summarization on a CMOS Ising machine

2026-01-16

AI Summary

This work addresses the demand for low-power, real-time extractive text summarization in resource-constrained environments by formulating the summarization task as an Ising model and efficiently implementing it on a CMOS coupled-oscillator Ising machine (COBI). To mitigate coefficient quantization imbalance, the authors propose a hardware-aware Ising formulation integrated with stochastic rounding, iterative refinement, and problem decomposition strategies, yielding a low-precision summarization pipeline that accommodates integer couplings and finite-precision constraints—generalizable to any k-of-n selection problem. Experiments on the CNN/DailyMail dataset demonstrate that the proposed approach achieves summary quality comparable to software-based Tabu search while delivering 3–4.5× faster inference and 2–3 orders of magnitude higher energy efficiency.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• extractive summarization
• CMOS Ising machine
• low-power computing
• edge devices
• energy efficiency

Innovation

• Ising machine
• extractive summarization
• CMOS hardware
• integer coupling
• energy-efficient NLP