Reshmi P Rajan

Work place: Department of Computer Science, Christ (Deemed to be University), S.G. Palya, Bengaluru, Karnataka 560029, India



Research Interests: Natural Language Processing, Machine Learning, Computer Vision


Reshmi P. Rajan, MCA, Mtech has around 16 years of R&D experience in IT industry. She is currently engaged as a Research Scholar in the Department of Computer Science, Christ (Deemed to be University). Her research interests include Natural Language Processing, Computer Vision, and Quantum Machine Learning. She has authored and published numerous Research articles in Journals and Conference proceedings.

Author Articles
Tangent Search Long Short Term Memory with Aadaptive Reinforcement Transient Learning based Extractive and Abstractive Document Summarization

By Reshmi P Rajan Deepa V. Jose Roopashree Gurumoorthy

DOI:, Pub. Date: 8 Dec. 2023

Text summarization is the process of creating a shorter version of a longer text document while retaining its most important information. There have been a number of methods proposed for text summarization, but the existing method does not provide better results and has a problem with sequence classification. To overcome these limitations, a tangent search long short term memory with adaptive reinforcement transient learning-based extractive and abstractive document summarization is proposed in this manuscript. In abstractive phase, the features of the extractive summary are extracted and then the optimal features are selected by Adaptive Flamingo Optimization (AFO). With these optimal features, the abstractive summary is generated. The proposed method is implemented in python. For extractive text summarization, the proposed method attains 42.11% ROUGE-1 Score, 23.55% ROUGE-2 score and 41.05% ROUGE-L score using Gigaword. Additionally, 57.13% ROUGE-1 Score, 28.35% ROUGE-2 score and 52.85% ROUGE-L score using DUC-2004 dataset. For abstractive text summarization the proposed method attains 47.05% ROUGE-1 Score, 22.02% ROUGE-2 score and 48.96% ROUGE-L score using Gigaword. Also, 35.13% ROUGE-1 Score, 20.35% ROUGE-2 score and 35.25% ROUGE-L score using DUC-2004 dataset.

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