Text summarization using residual-based temporal attention convolutional neural network
- Title
- Text summarization using residual-based temporal attention convolutional neural network
- Creator
- Rajan R.P.; Jose D.V.
- Description
- To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
- Source
- International Journal of Information Technology (Singapore)
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media B.V.
- Subject
- Classifier; Computational complexity; Entropy values; Feature extraction; Improved Initialization strategy-based aquila optimization algorithm; Residual-based temporal attention convolutional neural network
- Coverage
- Rajan R.P., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Jose D.V., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 25112104
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Rajan R.P.; Jose D.V., “Text summarization using residual-based temporal attention convolutional neural network,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14529.