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Topic modeling using latent Dirichlet allocation: A survey
We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …
relatively small subset. A computational tool is extremely needed to understand such a …
Knowledge discovery through directed probabilistic topic models: a survey
Graphical models have become the basic framework for topic based probabilistic modeling.
Especially models with latent variables have proved to be effective in capturing hidden …
Especially models with latent variables have proved to be effective in capturing hidden …
Simultaneously discovering and quantifying risk types from textual risk disclosures
Managers and researchers alike have long recognized the importance of corporate textual
risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from …
risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from …
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This paper offers an overview of the bibliometric study of the domain of library and
information science (LIS), with the aim of giving a multidisciplinary perspective of the topical …
information science (LIS), with the aim of giving a multidisciplinary perspective of the topical …
Text summarization using topic-based vector space model and semantic measure
The primary shortcoming associated with extractive text summarization is redundancy,
where more than one sentence representing a similar type of information are incorporated in …
where more than one sentence representing a similar type of information are incorporated in …
A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding
We provide a literature review about Automatic Text Summarization (ATS) systems. We
consider a citation-based approach. We start with some popular and well-known papers that …
consider a citation-based approach. We start with some popular and well-known papers that …
A new graph-based extractive text summarization using keywords or topic modeling
In graph-based extractive text summarization techniques, the weight assigned to the edges
of the graph is the crucial parameter for the sentence ranking. The weights associated with …
of the graph is the crucial parameter for the sentence ranking. The weights associated with …
Extractive text summarization using clustering-based topic modeling
Text summarization is the process of converting the input document into a short form,
provided that it preserves the overall meaning associated with it. Primarily, text …
provided that it preserves the overall meaning associated with it. Primarily, text …