2022
DOI: 10.21203/rs.3.rs-1938526/v1
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Text summarization based on semantic graphs: An abstract meaning representation graph-to-text deep learning approach

Abstract: Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework i… Show more

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Cited by 2 publications
(1 citation statement)
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“…This research highlights the importance of incorporating external knowledge sources in abstractive text summarization to improve the quality of generated summaries. Kauris, P. et al ( 2022) [36] The paper presents a novel framework for text summarization that combines semantic graph representations and deep learning predictions. The framework leverages abstract meaning representation models and explores various graph construction and transformation methods.…”
Section: Literature Reviewmentioning
confidence: 99%
“…This research highlights the importance of incorporating external knowledge sources in abstractive text summarization to improve the quality of generated summaries. Kauris, P. et al ( 2022) [36] The paper presents a novel framework for text summarization that combines semantic graph representations and deep learning predictions. The framework leverages abstract meaning representation models and explores various graph construction and transformation methods.…”
Section: Literature Reviewmentioning
confidence: 99%