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International Journal of Machine Learning and Cybernetics
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04-07-2024 | Original Article

A novel abstractive summarization model based on topic-aware and contrastive learning

Authors: Huanling Tang, Ruiquan Li, Wenhao Duan, Quansheng Dou, Mingyu Lu

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2024

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Abstract

The article presents a novel abstractive summarization model called ASTCL, which incorporates topic-aware and contrastive learning to enhance the quality of generated summaries. ASTCL addresses the limitations of existing Seq2Seq models by introducing a neural topic component based on NVDM-GSM to capture global semantic information and an evaluation model to mitigate training-testing phase discrepancies. The model demonstrates superior performance on various datasets, highlighting its effectiveness in generating more coherent and relevant summaries. Additionally, the article provides a comprehensive evaluation of the model through experiments and ablation studies, showcasing its robustness and potential for further advancements in the field of natural language processing.
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Metadata
Title
A novel abstractive summarization model based on topic-aware and contrastive learning
Authors
Huanling Tang
Ruiquan Li
Wenhao Duan
Quansheng Dou
Mingyu Lu
Publication date
04-07-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02263-8

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