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A comprehensive transfer news headline generation method based on semantic prototype transduction.

Ting-Huai Ma1, Xin Yu1, Huan Rong2

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Summary
This summary is machine-generated.

This study introduces a novel cross-domain transfer learning method for news headline generation. The approach effectively generates headlines for new domains without requiring labeled data, improving model usability.

Keywords:
data distribution alignmentsemantic prototype transductiontext generation modeltransfer learningzero-shot learning

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Current deep learning models for news headline generation are often domain-specific.
  • Training models on new domains requires costly labeled data, limiting generalization.

Purpose of the Study:

  • To develop a cross-domain transfer text generation method for news headlines.
  • To address the challenge of generating headlines for new domains lacking reference truth data.

Main Methods:

  • Domain data distribution alignment
  • Intermediate domain redistribution
  • Zero-shot learning semantic prototype transduction

Main Results:

  • The proposed method effectively generates headlines for target domains without labeled data.
  • Experimental results demonstrate superior performance over existing transfer learning methods.
  • The model shows a good domain transfer effect across various text generation evaluation indexes.

Conclusions:

  • The developed method significantly improves the usability of text generation models in real-world scenarios.
  • This approach overcomes the limitations of domain-specific training and data scarcity.
  • The method proves effective for unsupervised news headline generation in new domains.