Welcome to MLDA 2023

2nd International Conference on Machine Learning, NLP and Data Mining (MLDA 2023)

July 15-16, 2023, Virtual Conference

Accepted Papers
Graph-based Semantical Extractive Text Analysis

MinaSamizadeh, University of Delaware


In the past few decades, the availability of enormous text data necessitates us to adopt effective computational tools to explore 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 in this field 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 ranks websites in Google search engines, 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 final result. However, this algorithm neglects the semantic similarity between the different parts of the text and only considers the statistical measurements of the text such as tf-idf. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text to the algorithm. 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 step in generating the summaries to overcome coverage problems in summarization. Note that the proposed method can be applied in any language. In this work, we applied our method to Persian and English languages. Our empirical results show enhancement in the produced summaries compared to the baseline algorithm. The code is available on: https://github.com/minasmz/Persian-Summarization


Keyword Extraction;n-gramExtraction;Textsummarization;TopicClustering;Semantic Analysis.