e-Article

ATS: Auto Text Summarization using Natural Language Processing

Document Type

Conference

Author

Gohad, A.; Vyawahare, A.; Gupta, A.; Sharma, K.; Dhage, K.; Shelke, N.; Patni, J.

Source

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-5 Feb, 2024

Subject

Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Measurement
Computer science
Reviews
Navigation
Bibliographies
Buildings
Benchmark testing
Feature extraction
Natural language processing
Task analysis
Abstractive Summarization
Extractive Summarization
Stop World Removal
Clustering
Tokenization

Language

ISSN

2688-0288

Abstract

Automatic Text Summarization (ATS) is a crucial task in Natural Language Processing (NLP), seeking to distill critical information from voluminous textual data. This review provides a concise overview of extractive and abstractive summarization techniques. We explore the methodology used in various papers and extractive features usually implemented in building models like text summarization. Literature review, process, and comparative results are discussed, offering analysis of prominent approaches. This review is a quick reference for researchers and practitioners navigating the evolving landscape of auto-text summarization.

Online Access

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