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Abstractive text summarization of low-resourced languages using deep learning.

Nida Shafiq1, Isma Hamid1, Muhammad Asif1

  • 1Department of Computer Science, National Textile University, Faisalabad, Pakistan.

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Summary
This summary is machine-generated.

A new deep learning model significantly improves abstractive text summarization for Urdu, outperforming traditional machine learning methods. This advancement aids in processing Urdu

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Abstractive summarizationBERT2BERTLSTMPars-BERTSeq-to-SeqUrdu

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Automatic text summarization is crucial for managing information overload.
  • Extractive and abstractive methods are primary approaches to text summarization.
  • Abstractive summarization for low-resource languages like Urdu remains challenging.

Purpose of the Study:

  • To develop and evaluate a deep learning model for abstractive text summarization in Urdu.
  • To compare the proposed model's performance against established machine learning techniques.

Main Methods:

  • A deep learning model utilizing an encoder-decoder paradigm was developed.
  • The model was trained and tested on the Urdu 1 Million news dataset.
  • Performance was compared with Support Vector Machine (SVM) and Logistic Regression (LR) models.

Main Results:

  • The proposed deep learning model demonstrated superior performance compared to SVM and LR.
  • System-generated summaries were validated by Urdu language specialists.
  • The model showed significant improvement in accuracy for abstractive summarization.

Conclusions:

  • Deep learning offers a promising approach for abstractive text summarization in Urdu.
  • The developed model effectively addresses the challenges of summarizing Urdu text.
  • Further research in abstractive summarization for Urdu is warranted.