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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Related Experiment Video

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Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
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Abstractive summarization through the prism of decoding strategies.

Giacomo Frisoni1, Luca Ragazzi1, David Cohen1

  • 1University of Bologna, Department of Computer Science and Engineering, Via dell'Università, 50, Cesena, 47522, Emilia-Romagna, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|December 14, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing decoding strategies for abstractive summarization (AS) significantly boosts performance. This study provides crucial guidance on selecting effective techniques for various AS tasks and metrics, enhancing natural language generation quality.

Keywords:
Abstractive summarizationAutoregressive language modelsDecoding strategiesNatural language generation

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transformer-based language models (LMs) have accelerated natural language generation (NLG), particularly in abstractive summarization (AS).
  • Decoding strategies, crucial for summary quality, are often overlooked despite numerous heuristics and hyperparameters.
  • A lack of guidance exists for selecting optimal decoding techniques based on specific tasks and metrics.

Purpose of the Study:

  • To conduct a comprehensive comparative assessment of decoding-time techniques for abstractive summarization.
  • To provide guidance for informed decision-making regarding decoding strategies in AS.
  • To evaluate the effectiveness and efficiency of various decoding options across different AS tasks and LM architectures.

Main Methods:

  • Explored over 3,500 combinations of decoding settings with large-scale autoregressive and decoder-only LMs across six datasets.
  • Performed comparative assessment of decoding strategies for short, long, and multi-document AS.
  • Quantitatively measured performance using ten automatic metrics (semantic similarity, factuality, compression, redundancy, carbon footprint) and human evaluation.

Main Results:

  • Optimized decoding choices lead to substantial improvements in abstractive summarization performance.
  • Significant variations in effectiveness and efficiency were observed across different decoding strategies and tasks.
  • The study identified key decoding parameters influencing summary quality and resource utilization.

Conclusions:

  • Effective decoding strategy selection is critical for maximizing the performance of abstractive summarization models.
  • The findings offer valuable insights for researchers and practitioners in NLG.
  • Introduced Prism, a novel dataset to facilitate differentiable optimization of decoding options for AS.