Authors :
Vaishnav Bhardwaj; Shwetansh Sharma; Ajay Katiyan
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3kZnJ3v
DOI :
https://doi.org/10.5281/zenodo.6519400
Abstract :
Internet domain is flooded with text
information/ documents and it is difficult to get what kind
of information exactly we are looking. To get the
information we are looking for is head tic job as to find
right or wrong we have go through whole information or
text. This system Automatic text summarization
summarizes the whole text or paragraph and give result
in form of natural language using machine learning. This
paper aim to present a process of summarization by using
Machine Learning algorithms based on extraction of text
based on their features the data to be summaries. There
are two types of features the algorithms looks for one the
frequency of element in the text and second is the
linguistic extracted structure of the text. We also give
some computational results achieved by applying our
summarizer to certain dataset, which is compare to some
baseline summary processes.
Internet domain is flooded with text
information/ documents and it is difficult to get what kind
of information exactly we are looking. To get the
information we are looking for is head tic job as to find
right or wrong we have go through whole information or
text. This system Automatic text summarization
summarizes the whole text or paragraph and give result
in form of natural language using machine learning. This
paper aim to present a process of summarization by using
Machine Learning algorithms based on extraction of text
based on their features the data to be summaries. There
are two types of features the algorithms looks for one the
frequency of element in the text and second is the
linguistic extracted structure of the text. We also give
some computational results achieved by applying our
summarizer to certain dataset, which is compare to some
baseline summary processes.
