mlsanityData to 7 Oct 2026

Latent Prompt Tuning for Text Summarization

arXiv 2211.01837 ↗First posted 3 Nov 2022cs.CLcited by 6

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Abstract (arXiv)

Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties of generated summaries and potentially improve summarization quality (since more information are given). Unfortunately, control signals are not already available during inference time. In this paper, we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled (without control signals) modes. During training, Lotus learns latent prompt representations from prompts with gold control signals using a contrastive learning objective. Experiments show Lotus in uncontrolled mode consistently improves upon strong (uncontrollable) summarization models across four different summarization datasets. We also demonstrate generated summaries can be controlled using prompts with user specified control tokens.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

21 of 34 references.

References · 21

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9 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 5.

6 papers.

  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  2. 2024Prompt-Based Length Controlled Generation with Multiple Control Typescited by 19
  3. 2023Prompt-Based Length Controlled Generation with Reinforcement Learningcited by 14
  4. 2024LaPA: Latent Prompt Assist Model For Medical Visual Question Answeringcited by 12
  5. 2024Hansel: Output Length Controlling Framework for Large Language Modelscited by 2
  6. 2024FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizerscited by 1