Sentence-Level Enhanced Graph-BERT (SEG-BERT) for Extractive Text Summarization

Background: The exponential growth of digital text across news media and online platforms has intensified the need for automatic summarization models that capture both semantic nuances and document-level structure. Methods: This study introduces Sentence-level Enhanced Graph-BERT (SEG-BERT), a hybrid extractive summarization framework that combines BERT-base contextual embeddings with graph convolutional networks (GCNs) and Sentence-level Positional Encoding (SLPE) to model documents as dynamic semantic graphs. Each sentence is encoded with BERT, enriched with trainable positional encodings, connected through a learned attention-based adjacency matrix, and refined via stacked GCN layers before a classification head assigns sentence salience scores for extractive selection. Results: SEG-BERT was trained on 287,113 news articles from the CNN/DailyMail corpus in a single-epoch proof-of-concept experiment and evaluated on a held-out sample of 1,000 unseen documents using ROUGE metrics, achieving F1 scores of 42.1 (ROUGE-1), 19.5 (ROUGE-2), and 40.2 (ROUGE-L). Conclusions: The results demonstrate the viability of sentence-level graph-augmented transformer architectures for large-scale extractive summarization and provide a solid foundation for SEG-BERT multi-epoch optimization and cross-domain evaluation.