Recent Trends in Deep Learning Based Abstractive Text Summarization
Neha Rane1, Sharvari Govilkar2

1Neha Rane, Research Scholar, Department of Computer Engineering, Pillai College of Engineering, University of Mumbai, Panvel  India.
2Dr. Sharvari Govilkar, Associate Professor, Department of Computer Engineering, Pillai College of Engineering, University of Mumbai, Panvel  India.

Manuscript received on 4 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 3108-3115 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4996098319/2019©BEIESP | DOI: 10.35940/ijrte.C4996.098319
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Abstract: With the rapid growth of cyberspace and the appearance of knowledge exploration era, good text summarization method is vital to reduce the large data. Text summarization is the mechanism of extracting the important information which gives us an overall abstract or summary of the entire document and also reduces the size of the document. It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document. Text Summarization has become an important and timely tool for assisting and interpreting text information. It is generally distinguished into: Extractive and Abstractive. The first method directly chooses and outputs the relevant sentences in the original document; on the other hand, the latter rewrites the original document into summary using NLP techniques. From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation. This paper gives a brief survey of the distinct attempts undertaken in the field of abstractive summarization. It collectively summarizes the numerous technologies, difficulties and problem of abstractive summarization.
Keywords: Abstractive Summary, Deep learning, Structure Based Approach, Semantic Based Approach, Text Summarization,

Scope of the Article:
Deep Learning