Open AccessAn Exhaustive Survey on Automatic Text Summarization Using Machine Learning Approches
Author(s) -
N. Abinaya,
Rahul Anand,
T. Arunkumar,
Sameema Begam S
Publication year - 2021
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v18si05/web18299
Subject(s) - automatic summarization , computer science , natural language processing , artificial intelligence , key (lock) , the internet , information retrieval , text graph , natural language , multi document summarization , world wide web , computer security
Automatic Text Summarization (ATS) is the key challenge in the area of Natural Language Processing (NLP). It deals with generalizing a summary from a given text without losing the vital information. This is a contemporary area because of exponential content growth in internet and applied in summarizing the content available in books, newsletters, internal document analysis, patent research, e-learning etc. Various machine learning approaches are used in order to achieve the performance of human-generated summaries. The system fails to perform at few areas like checking grammatical errors and paraphrasing the sentences after the summary creation. This work provides a brief view on methods and approaches used in ATS.