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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization.

Muhammad Umair¹, Iftikhar Alam¹, Atif Khan²

¹Department of Computer Science, City University of Science and Information Technology, Peshawar 25000, Pakistan.

Computational Intelligence and Neuroscience

|December 2, 2022

Summary

This summary is machine-generated.

This study introduces N-GPETS, a novel neural model for extractive summarization that enhances cross-sentence associations using BERT and heterogeneous graph attention networks. The model achieves favorable results on benchmark datasets, outperforming existing methods.

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Area of Science:

Natural Language Processing
Artificial Intelligence
Machine Learning

Background:

Extractive summarization relies on identifying salient sentences and modeling cross-sentence associations.
Transformer-based models like BERT and graph attention networks (GAT) show promise in capturing these associations.

Purpose of the Study:

To propose a novel neural model, N-GPETS, for extractive summarization.
To leverage the strengths of BERT and GAT for improved inter-sentence interaction.

Main Methods:

N-GPETS combines a heterogeneous graph attention network with the BERT model and TF-IDF values.
It incorporates semantic word nodes to enhance sentence linking and inter-sentence interaction.
The BERT encoder is integrated at the graph initialization stage for a feature-rich model.

Main Results:

Empirical results on the CNN/DM dataset demonstrate favorable performance of N-GPETS.
The model shows improvement compared to other heterogeneous graph structures with and without BERT.

Conclusions:

N-GPETS represents a novel approach to extractive summarization by integrating BERT and heterogeneous graph attention.
The proposed model effectively captures complex inter-sentence relationships for improved summary generation.