BERT Extractive Summarization with K-Means Clustering in Scientific Paper Samosir Feliks Victor Parningotan Primary Author mixed material bibliography Bandung Maranatha University Press 2022 Indonesia

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hlm : 202-217 Abstract This study aims to propose methods and models for extractive text summarization with contextual embedding. To build this model, a combination of traditional machine learning algorithms such as K-Means Clustering and the latest BERT-based architectures such as Sentence-BERT (SBERT) is carried out. The contextual embedding process will be carried out at the sentence level by SBERT. Embedded sentences will be clustered and the distance calculated from the centroid. The top sentences from each cluster will be used as summary candidates. The dataset used in this study is a collection of scientific journals from NeurIPS. Performance evaluation carried out with ROUGE-L gave a result of 15.52% and a BERTScore of 85.55%. This result surpasses several previous models such as PyTextRank and BERT Extractive Summarizer. The results of these measurements prove that the use of contextual embedding is very good if applied to extractive text summarization which is generally done at the sentence level. Informatika Sistem Informasi JUTISI 24432210 Perpustakaan Teknik UPI YAI JUTISI V8N1 April 2022 JUTISI7a-017 Perpustakaan FT UPI YAI JUTISI V8N1 April 2022 JUTISI7b-017 Perpustakaan FT UPI YAI JUTISI V8N1 April 2022 cover_issue_174_en_US.png.png 59312 2023-03-01 10:18:05 2023-03-01 10:18:05 machine generated