Text Summary Generation Techniques
Yashasvi Swapnesh Kumar Parikh1, Narsingani Amisha Darshakbhai2, Hetal Gaudani3
1Yashasvi Swapnesh Kumar Parikh, Computer Engineering, G. H. Patel College of Engineering & Technology, Vallabh Vidhyanagar, Anand, India.
2Narsingani Amisha Darshakbhai, Computer Engineering, G. H. Patel College of Engineering & Technology, Vallabh Vidhyanagar, Anand, India.
3Hetal Gaudani, Computer Engineering, G. H. Patel College of Engineering & Technology, Vallabh Vidhyanagar, Anand, India.
Manuscript received on 26 April 2020 | Revised Manuscript received on 08 May 2020 | Manuscript Published on 22 May 2020 | PP: 50-54 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10160597S20 | DOI: 10.35940/ijitee.G1016.0597S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pattern Recognition is pertinent field in autonomous text summarization for extraction of features from relative and non relative text documents. Here we provide empirical evidence that the method of Deep learning using RNN outperforms various techniques in terms of speed as well as metrics in abstractive summarization of multi-modal documents. We performed observational analysis on over 8 different techniques documented.
Keywords: Automatic Summarization, Natural Language Processing, Extractive Summary.
Scope of the Article: Text Mining