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A Novel Keyword Generation Model Based on Topic-Aware and Title-Guide.

Jialin Ma1, Jieyi Cheng1, Yue Zhang1

  • 1College of Computer Science, Huaiyin Institute of Technology, Huaian 223003, China.

Computational Intelligence and Neuroscience
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Summary

This study introduces a novel Keyword Generation Model (KGM-TT) that improves keyword extraction by incorporating topic information and title guidance. The model generates relevant keywords not present in the original text, outperforming existing methods.

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Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Traditional keyword extraction methods struggle with identifying keywords not present in the source text.
  • Existing approaches often neglect semantic information and topic relevance during keyword generation.

Purpose of the Study:

  • To introduce a novel Keyword Generation Model based on Topic-aware and Title-guide (KGM-TT).
  • To enhance automatic keyword generation by incorporating latent topic information and title semantics.
  • To overcome limitations of traditional methods in generating out-of-document keywords.

Main Methods:

  • Utilized a neural topic model to identify latent topic words.
  • Employed a hierarchical encoder with an attention mechanism to process document titles and content.
  • Implemented a recurrent neural network with attention and replication mechanisms for keyword generation.

Main Results:

  • The KGM-TT model demonstrated the ability to generate keywords not present in the source document.
  • Incorporated topic information and title semantics to assist keyword generation.
  • Achieved an F1 score approximately 10% higher than CopyRNN and CopyCNN models.

Conclusions:

  • The KGM-TT model offers a significant advancement in automatic keyword generation.
  • Topic-aware and title-guided approaches enhance the quality and relevance of extracted keywords.
  • The model's performance indicates its potential for improved information retrieval and document indexing.