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Related Experiment Video

Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

Deep learning for text summarization using NLP for automated news digest.

K M Rani Krishna1, K Somasundaram2, P Arulmozhivarman3

  • 1Department of Mathematics, Amrita School of Physical Science, Amrita Vishwa Vidyapeetham, Coimbatore, India.

Scientific Reports
|October 17, 2025
PubMed
Summary

This study compares deep learning models for text summarization, finding PEGASUS offers the best performance based on Rouge scores. The research addresses challenges in natural language processing for effective text condensation.

Keywords:
Abstractive text-summarizationBART CNN-largeDeep learningEDAModel evaluationPEGASUS-largeT5-baseT5-largeText summarization

Related Experiment Videos

Last Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Machine Learning

Background:

  • Text summarization is crucial for condensing information while preserving meaning.
  • Deep learning models face challenges like semantic understanding and handling long documents.
  • Existing deep learning approaches offer benefits such as time savings and personalization.

Purpose of the Study:

  • To propose and evaluate deep learning models for text summarization.
  • To compare the performance of T5-base, T5-large, BART CNN, and PEGASUS models.
  • To identify the model yielding the highest quality summaries based on evaluation metrics.

Main Methods:

  • Data preprocessing and exploratory data analysis (EDA).
  • Implementation and training of T5-base, T5-large, BART CNN, and PEGASUS models.
  • Evaluation using Rouge and BLUE scores to assess summarization quality.

Main Results:

  • The study quantifies the performance of each deep learning model.
  • Rouge and BLUE scores were calculated post-training to measure effectiveness.
  • Comparative analysis focused on identifying the model with the highest Rouge score.

Conclusions:

  • Deep learning models show promise in advancing text summarization techniques.
  • Model performance varies, with PEGASUS identified as a strong performer.
  • Further research is ongoing to address challenges in deep learning-based summarization.