  In the past few decades, there has been an explosion in the amount of
available data produced from various sources with different topics. The
availability of this enormous data necessitates us to adopt effective
computational tools to explore the data. This leads to an intense growing
interest in the research community to develop computational methods focused on
processing this text data. A line of study focused on condensing the text so
that we are able to get a higher level of understanding in a shorter time. The
two important tasks to do this are keyword extraction and text summarization.
In keyword extraction, we are interested in finding the key important words
from a text. This makes us familiar with the general topic of a text. In text
summarization, we are interested in producing a short-length text which
includes important information about the document. The TextRank algorithm, an
unsupervised learning method that is an extension of the PageRank (algorithm
which is the base algorithm of Google search engine for searching pages and
ranking them) has shown its efficacy in large-scale text mining, especially for
text summarization and keyword extraction. this algorithm can automatically
extract the important parts of a text (keywords or sentences) and declare them
as the result. However, this algorithm neglects the semantic similarity between
the different parts. In this work, we improved the results of the TextRank
algorithm by incorporating the semantic similarity between parts of the text.
Aside from keyword extraction and text summarization, we develop a topic
clustering algorithm based on our framework which can be used individually or
as a part of generating the summary to overcome coverage problems.
