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

Abstract

The extractive summarization approach involves selecting the source document’s salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.

AI-driven Generation of News Summaries Leveraging GPT and Pegasus Summarizer for Efficient Information Extraction

“…According to [4], extractive text summarization is the approach of selecting salient sentences from a source document to create a summary. They propose a new neural model called N-GPETS for extractive text summarization.…”

“…Data Set Metrics Result/Observation N-GPETS [19] Extractive ----Favorable results obtained Transformers [7] Table 1: Summary table of selected information…”

Retracted: N‐GPETS: Neural Attention Graph‐Based Pretrained Statistical Model for Extractive Text Summarization

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”