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View all- Rao AAithal SSingh S(2024)Single-Document Abstractive Text Summarization: A Systematic Literature ReviewACM Computing Surveys10.1145/370063957:3(1-37)Online publication date: 11-Nov-2024
Effect of Semantic Content Generalization on Pointer Generator Network in Text Summarization
Pages 72 - 76
Abstract
Semantic content generalization is a method for text summarization that reduces the difficulty of training of neural networks by replacing some phrases such as named entities with generalized terms. The semantic content generalization has achieved remarkable results in enhancing the performance of the sequence to sequence attention model. Besides that, the pointer generator network could ease the training of the summarization based on a mechanism that copies words from the original text, which shares a similar idea with semantic content generalization. The purpose of this work is to test and verify the effect of semantic content generalization on the pointer generator network. Therefore, we use the preprocessing of semantic content generalization and then combine it with the pointer generator network. We examine the performance through an experiment using CNN/DailyMail dataset. From the experiment, we found that the semantic content generalization can improve the performance of the pointer generator network.
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Index Terms
- Effect of Semantic Content Generalization on Pointer Generator Network in Text Summarization
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November 2020
492 pages
ISBN:9781450389228
DOI:10.1145/3428757
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- Johannes Kepler University, Linz, Austria
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Association for Computing Machinery
New York, NY, United States
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Published: 27 January 2021
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iiWAS '20
iiWAS '20: The 22nd International Conference on Information Integration and Web-based Applications & Services
November 30 - December 2, 2020
Chiang Mai, Thailand
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View all- Rao AAithal SSingh S(2024)Single-Document Abstractive Text Summarization: A Systematic Literature ReviewACM Computing Surveys10.1145/370063957:3(1-37)Online publication date: 11-Nov-2024
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