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.

本文介绍了针对文本数据的关键词提取和文本摘要的处理方法，介绍了一种基于 TextRank 算法的无监督学习方法，在其基础上提高了算法的效率，并针对其忽略了不同部分的语义相似性进行了改进。此外，还开发了一种基于该框架的主题聚类算法，可单独使用或作为生成摘要的一部分来解决文本覆盖问题。

基于图的语义提取文本分析

Graph-based Semantical Extractive Text Analysis

This article presents new alternatives to the similarity function for the
TextRank algorithm for automatic summarization of texts. We describe the
generalities of the algorithm and the different functions we propose. Some of
these variants achieve a significative improvement using the same metrics and
dataset as the original publication.

该文章提出了一些新的相似度函数的替代方案，用于 TextRank 算法进行文本自动摘要。作者描述了该算法的概况和不同变体，并指出其中一些变体在使用相同度量和数据集的情况下取得了显著的改进。