Article
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Abstractive Summarization Model for Summarizing Scientific Article
Version 1
: Received: 16 May 2024 / Approved: 16 May 2024 / Online: 16 May 2024 (18:21:24 CEST)
How to cite: Ülker, M.; Özer, A. B. Abstractive Summarization Model for Summarizing Scientific Article. Preprints 2024, 2024051123. https://doi.org/10.20944/preprints202405.1123.v1 Ülker, M.; Özer, A. B. Abstractive Summarization Model for Summarizing Scientific Article. Preprints 2024, 2024051123. https://doi.org/10.20944/preprints202405.1123.v1
Abstract
The rapid growth in scientific article publications allows us to access articles as soon as possible. Therefore, automatic summarization systems (ATSs) are widely preferred. In most studies, the en-tire source document is expected to be summarized, just as it would be summarized by a human. Summarizing long articles, such as scientific articles, is quite difficult due to token restraint and extraction of scientific words. To address this problem, a novel Graph-Based Abstractive Summa-rization (GBAS) model is proposed, which is a novel scientific text summarization model based on SciBERT and the graph transformer network (GTN). The document's integrity is maintained since the SciIE system uses the graph structure to create a terminology-based document structure. Therefore, long documents are also summarized. The proposed model is compared with baseline models and human evaluation. Human evaluation results show that the results of the proposed model are informative, fluent, and consistent with the ground-truth summary. The experimental results indicate that the proposed model outperforms baseline models with a 37.10 and 34.96 ROUGE-L score.
Keywords
Text summarization; Abstractive method; SciBERT; SciIE; Graph transformer
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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