Knowledge-Enhanced Transformer Graph Summarization (KETGS): Integrating Entity and Discourse Relations for Advanced Extractive Text Summarization

Aytuğ Onan () and Hesham Alhumyani
Additional contact information
Aytuğ Onan: Department of Computer Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir 35620, Turkey
Hesham Alhumyani: Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Mathematics, 2024, vol. 12, issue 23, 1-25

Abstract: The rapid proliferation of textual data across multiple sectors demands more sophisticated and efficient techniques for summarizing extensive texts. Focusing on extractive text summarization, this approach zeroes in on choosing key sentences from a document, providing an essential method for handling extensive information. While conventional methods often miss capturing deep semantic links within texts, resulting in summaries that might lack cohesion and depth, this paper introduces a novel framework called Knowledge-Enhanced Transformer Graph Summary (KETGS). Leveraging the strengths of both transformer models and Graph Neural Networks, KETGS develops a detailed graph representation of documents, embedding linguistic units from words to key entities. This structured graph is then navigated via a Transformer-Guided Graph Neural Network (TG-GNN), dynamically enhancing node features with structural connections and transformer-driven attention mechanisms. The framework adopts a Maximum Marginal Relevance (MMR) strategy for selecting sentences. Our evaluations show that KETGS outshines other leading extractive summarization models, delivering summaries that are more relevant, cohesive, and concise, thus better preserving the essence and structure of the original texts.

Keywords: document summarization; extractive summarization; graph neural networks; text summarization; transformer models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3638/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3638/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3638-:d:1525907

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().