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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Text Summarization Techniques: A Brief Survey, Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
Text Summarization Techniques: A Brief Survey, Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
Department of Computer Science Faculty Publications
In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.
A Knowledge-Based Topic Modeling Approach For Automatic Topic Labeling, Mehdi Allahyari, Seyedamin Pouriyeh, Krys Kochut, Hamid Reza Arabnia
A Knowledge-Based Topic Modeling Approach For Automatic Topic Labeling, Mehdi Allahyari, Seyedamin Pouriyeh, Krys Kochut, Hamid Reza Arabnia
Department of Computer Science Faculty Publications
Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality …