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There has been a significant effort by the research community to address theproblem of providing methods to organize documentation with the help ofinformation Retrieval methods. In this report paper, we present severalexperiments with some stream analysis methods to explore streams of textdocuments. We use only dynamic algorithms to explore, analyze, and organize theflux of text documents. This document shows a case study with developedarchitectures of a Text Document Stream Organization, using incrementalalgorithms like Incremental TextRank, and IS-TFIDF. Both these algorithms arebased on the assumption that the mapping of text documents and theirdocument-term matrix in lower-dimensional evolving networks provides fasterprocessing when compared to batch algorithms. With this architecture, and byusing FastText Embedding to retrieve similarity between documents, we comparemethods with large text datasets and ground truth evaluation of clusteringcapacities. The datasets used were Reuters and COVID-19 emotions. The resultsprovide a new view for the contextualization of similarity when approachingflux of documents organization tasks, based on the similarity between documentsin the flux, and by using mentioned algorithms.

Contextualization for the Organization of Text Documents Streams