Affiliations: [a] Department of Computer Science & Engineering, National Institute of Technology, Hamirpur (H.P.), India
| [b] Department of Computer Science & Engineering, Galgotias University Greater Noida, Uttar Pradesh, India
| [c] School of Computing and Information Technology, REVA University, Bengaluru, India
| [d] HoD-Cyber Security and Internet of Things, Department of Computer Science & Engineering, Presidency University, Bangalore, India
Correspondence:
[*]
Corresponding author. Jyoti Srivastava, Assistant Professor, Department of Computer Science & Engineering, National Institute of Technology, Hamirpur (H.P.), India. E-mail: jyoti.s@nith.ac.in.
Note: [1] Text Summarization using Modified Generative Adversarial Network!
Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively.
Keywords: Text summarization, convolutional neural network, bidirectional gated recurrent unit
