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Related Experiment Video

Updated: May 25, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Ontology-based prompt tuning for news article summarization.

A R S Silva¹, Y H P P Priyadarshana¹

¹Informatics Institute of Technology, Colombo, Sri Lanka.

Frontiers in Artificial Intelligence

|February 26, 2025

Summary

This study introduces ontology-based prompt tuning and abstractive summarization for better news summaries. The novel framework significantly improves accuracy and contextual relevance over existing methods.

Keywords:

knowledge representation natural language processing (NLP)ontology prompt tuning text summarization

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Area of Science:

Natural Language Processing (NLP)
Artificial Intelligence
Knowledge Representation

Background:

Current abstractive summarization methods often produce generic or inaccurate news summaries.
Extractive summarization lacks coherence and contextual richness.
Limited integration of domain-specific knowledge in existing NLP models.

Discussion:

A novel framework combining ontology-based prompt tuning with abstractive summarization is proposed.
Leveraging ontological knowledge enables fine-tuning of the summarization process for domain relevance.
This approach enhances the nuanced understanding of text for more accurate and coherent summaries.

Key Insights:

The proposed model significantly outperforms state-of-the-art methods like BART, BERT, and GPT-3.5.
Achieved 5.1% higher ROUGE-1 and 9.8% higher ROUGE-L scores.
Demonstrated significant improvements in F1 (6.7%), precision (3.9%), and recall (4.8%).

Outlook:

Ontology-based prompt tuning offers a robust solution for high-quality, domain-specific news summarization.
Future work can explore broader applications of ontological integration in NLP tasks.
Potential for enhanced information retrieval and knowledge discovery through improved summarization.