A Review: Abstractive Text Summarization Techniques using NLP

Today's world is getting flooded with an increasing amount of articles and links to choose from. As this data grows, the importance of semantic density does as well. How can one say the most important things in the shortest amount of time? Having a generated summary lets one decide whether they...

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Bibliographic Details
Main Authors: Batra, Pooja, Chaudhary, Sarika, Bhatt, Kavya, Varshney, Saloni, Verma, Srashti
Format: Conference Proceeding
Language:English
Subjects:
Abstractive text summarization
Attention Mechanism
Generators
LSTM
Machine learning
Machine learning algorithms
Recurrent neural networks
RNN
Semantics
Task analysis
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Summary:Today's world is getting flooded with an increasing amount of articles and links to choose from. As this data grows, the importance of semantic density does as well. How can one say the most important things in the shortest amount of time? Having a generated summary lets one decide whether they want to deep dive further or not. Conversion of lengthy texts into short and meaningful sentences is the main idea behind text summarization. To achieve this, various algorithms are present. Machine Learning models are trained, first to understand the given document and then create a summary of it. These models achieve this task either by extracting important words out of the document or by creating human-like sentences to form the summary.
ISSN:2642-7354
DOI:10.1109/ICACCM50413.2020.9213079