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Automatic summarization of Instagram social network posts by combining semantic and statistical approaches
Poster Presentation
Authors
Department of Computer Engineering, Khatam University, Tehran, Iran
Abstract
The increasing spread of data and text
documents such as articles, web pages, books, posts on
social networks, etc. on the Internet, creates a
fundamental challenge in various fields of text processing
under the title of ``automatic text summarization''.
Manual processing and summarization of large volumes
of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text
summarization systems are divided into extractive and
abstract categories. In the extractive summarization
method, the final summary of a text document is
extracted from the important sentences of the same
document without any kind of change. In this method, it
is possible to repeat a series of sentences repeatedly and
interfere with pronouns. But in the abstract
summarization method, the final summary of a textual
document is extracted from the meaning of the sentences
and words of the same document or other documents.
Many of the performed works have used extraction
methods or abstracts to summarize the collection of web
documents, each of which has advantages and
disadvantages in the results obtained in terms of
similarity or size. In this research, by developing a
crawler, extracting the popular text posts of Instagram
social network, suitable pre-processing and combining
the set of extractive and abstract algorithms, the
researcher showed how to use each of the abstract
algorithms. and used extraction as a supplement to
increase the accuracy and accuracy of another
algorithm. Observations made on 820 popular text posts
on the Instagram social network show the accuracy
(80%) of the proposed system.
documents such as articles, web pages, books, posts on
social networks, etc. on the Internet, creates a
fundamental challenge in various fields of text processing
under the title of ``automatic text summarization''.
Manual processing and summarization of large volumes
of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text
summarization systems are divided into extractive and
abstract categories. In the extractive summarization
method, the final summary of a text document is
extracted from the important sentences of the same
document without any kind of change. In this method, it
is possible to repeat a series of sentences repeatedly and
interfere with pronouns. But in the abstract
summarization method, the final summary of a textual
document is extracted from the meaning of the sentences
and words of the same document or other documents.
Many of the performed works have used extraction
methods or abstracts to summarize the collection of web
documents, each of which has advantages and
disadvantages in the results obtained in terms of
similarity or size. In this research, by developing a
crawler, extracting the popular text posts of Instagram
social network, suitable pre-processing and combining
the set of extractive and abstract algorithms, the
researcher showed how to use each of the abstract
algorithms. and used extraction as a supplement to
increase the accuracy and accuracy of another
algorithm. Observations made on 820 popular text posts
on the Instagram social network show the accuracy
(80%) of the proposed system.
Keywords
text summarization; extractive approach; abstract approach; natural language processing; social networks
Proceeding Title [Persian]
Automatic summarization of Instagram social network posts by combining semantic and statistical approaches
Authors [Persian]
Abstract [Persian]
The increasing spread of data and text
documents such as articles, web pages, books, posts on
social networks, etc. on the Internet, creates a
fundamental challenge in various fields of text processing
under the title of ``automatic text summarization''.
Manual processing and summarization of large volumes
of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text
summarization systems are divided into extractive and
abstract categories. In the extractive summarization
method, the final summary of a text document is
extracted from the important sentences of the same
document without any kind of change. In this method, it
is possible to repeat a series of sentences repeatedly and
interfere with pronouns. But in the abstract
summarization method, the final summary of a textual
document is extracted from the meaning of the sentences
and words of the same document or other documents.
Many of the performed works have used extraction
methods or abstracts to summarize the collection of web
documents, each of which has advantages and
disadvantages in the results obtained in terms of
similarity or size. In this research, by developing a
crawler, extracting the popular text posts of Instagram
social network, suitable pre-processing and combining
the set of extractive and abstract algorithms, the
researcher showed how to use each of the abstract
algorithms. and used extraction as a supplement to
increase the accuracy and accuracy of another
algorithm. Observations made on 820 popular text posts
on the Instagram social network show the accuracy
(80%) of the proposed system.
documents such as articles, web pages, books, posts on
social networks, etc. on the Internet, creates a
fundamental challenge in various fields of text processing
under the title of ``automatic text summarization''.
Manual processing and summarization of large volumes
of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text
summarization systems are divided into extractive and
abstract categories. In the extractive summarization
method, the final summary of a text document is
extracted from the important sentences of the same
document without any kind of change. In this method, it
is possible to repeat a series of sentences repeatedly and
interfere with pronouns. But in the abstract
summarization method, the final summary of a textual
document is extracted from the meaning of the sentences
and words of the same document or other documents.
Many of the performed works have used extraction
methods or abstracts to summarize the collection of web
documents, each of which has advantages and
disadvantages in the results obtained in terms of
similarity or size. In this research, by developing a
crawler, extracting the popular text posts of Instagram
social network, suitable pre-processing and combining
the set of extractive and abstract algorithms, the
researcher showed how to use each of the abstract
algorithms. and used extraction as a supplement to
increase the accuracy and accuracy of another
algorithm. Observations made on 820 popular text posts
on the Instagram social network show the accuracy
(80%) of the proposed system.
Keywords [Persian]
text summarization، extractive approach، abstract approach، natural language processing، social networks