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T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions.
- Source :
- Applied Sciences (2076-3417); Jun2023, Vol. 13 Issue 12, p7111, 16p
- Publication Year :
- 2023
-
Abstract
- Text summarization is a prominent task in natural language processing (NLP) that condenses lengthy texts into concise summaries. Despite the success of existing supervised models, they often rely on datasets of well-constructed text pairs, which can be insufficient for languages with limited annotated data, such as Chinese. To address this issue, we propose a semi-supervised learning method for text summarization. Our method is inspired by the cycle-consistent adversarial network (CycleGAN) and considers text summarization as a style transfer task. The model is trained by using a similar procedure and loss function to those of CycleGAN and learns to transfer the style of a document to its summary and vice versa. Our method can be applied to multiple languages, but this paper focuses on its performance on Chinese documents. We trained a T5-based model and evaluated it on two datasets, CSL and LCSTS, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Subjects :
- TEXT summarization
NATURAL language processing
SUPERVISED learning
CHINESE language
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 13
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 164592531
- Full Text :
- https://doi.org/10.3390/app13127111