Authors :
Naman Garg, Tanmay Jain
Volume/Issue :
Volume 4 - 2019, Issue 8 - August
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://bit.ly/2HpkChQ
Abstract :
Within recent times, there has been a need
for text summary generators to cut short lengthy
academic or non-academic texts for effective reading. In
recent times, there have been many techniques that
deploy text summarization yet, their speed, efficiency
and scalability is a concern. This is a challenge in
natural language processing. The need for text
summarization is necessary with the number of texts
and documents which are available online. In this
paper, we have proposed a new efficient technique of
text summarization which uses text rank and lexical
index scores to provide a coherent legible and concise
text. Experimental results show that the technique is
promising in solving the challenges faced by
summarization systems in NLP. Furthermore, this
technique can be extended further for generating bullet
points, abstracts and mental maps with more semantic
links.
Keywords :
Text Mining, Summarization, Text Rank.
Within recent times, there has been a need
for text summary generators to cut short lengthy
academic or non-academic texts for effective reading. In
recent times, there have been many techniques that
deploy text summarization yet, their speed, efficiency
and scalability is a concern. This is a challenge in
natural language processing. The need for text
summarization is necessary with the number of texts
and documents which are available online. In this
paper, we have proposed a new efficient technique of
text summarization which uses text rank and lexical
index scores to provide a coherent legible and concise
text. Experimental results show that the technique is
promising in solving the challenges faced by
summarization systems in NLP. Furthermore, this
technique can be extended further for generating bullet
points, abstracts and mental maps with more semantic
links.
Keywords :
Text Mining, Summarization, Text Rank.
