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arXiv · 2611.06371

Training-Free Character-Length Control in Summarization with Diffusion Language Models

Steffen Castle·Nils Feldhus·Christopher Ebert·Leonhard Hennig·Sebastian Möller
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Abstract

Summarization with a fixed character length is important for tasks such as headline and article preview generation, where text must fit an exact space. Autoregressive language models (LMs) struggle with precise character-length control: training-based methods require task-specific fine-tuning, while zero-shot prompting strategies achieve only approximate compliance. We propose DELTA (Diffusion Expected Length Targeting Algorithm), a training-free inference-time method for masked diffusion LMs that estimates expected character length at each denoising step and dynamically inserts or removes masked positions to steer toward a target length. DELTA requires no fine-tuning and is designed for off-the-shelf masked diffusion LMs. Across three summarization benchmarks, it reduces character-length errors by an order of magnitude compared to baselines while maintaining competitive summary quality in both automatic and human evaluation.

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Steffen Castle, Nils Feldhus, Christopher Ebert, Leonhard Hennig, Sebastian Möller. 2026-11-04. Training-Free Character-Length Control in Summarization with Diffusion Language Models. https://doi.org/10.18653/2026.emnlp-main.121

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