JOURNAL ARTICLE

Recurrent Neural Network based Text Summarization Techniques by Word Sequence Generation

Abstract

Word sequence prediction is the word processing method that can be very useful for generating text summary by reducing the number of keystrokes when in need for typing words. So, mostly typing and summarizing a large text summary document is very complicated and is time consuming process. With a large and huge amount of data circulating in this digital data world, there is necessary to create and develop some machine learning algorithms that can automatically generate accurate summaries. Types of Neural Network and deep learning field techniques are the most popular and efficient method for automatic text summarization. This paper provides various methods and different datasets that may be used to generate text summaries automatically which reduces the time to manually type and summarize large text documents.

Keywords:
Automatic summarization Computer science Word (group theory) Artificial intelligence Artificial neural network Natural language processing Recurrent neural network Process (computing) Sequence (biology) Field (mathematics) Multi-document summarization Deep learning Text graph Information retrieval

Metrics

34
Cited By
4.37
FWCI (Field Weighted Citation Impact)
39
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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