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

Benchmarks and datasets

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Reported metrics

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Key takeaways

• 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.

Researcher actions

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Caveats

• Generated from abstract + metadata only; no PDF parsing.
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Contribution summary

• 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.

Why it matters for eval

• 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.