There has been a significant effort by the research community to address the
problem of providing methods to organize documentation with the help of
information Retrieval methods. In this report paper, we present several
experiments with some stream analysis methods to explore streams of text
documents. We use only dynamic algorithms to explore, analyze, and organize the
flux of text documents. This document shows a case study with developed
architectures of a Text Document Stream Organization, using incremental
algorithms like Incremental TextRank, and IS-TFIDF. Both these algorithms are
based on the assumption that the mapping of text documents and their
document-term matrix in lower-dimensional evolving networks provides faster
processing when compared to batch algorithms. With this architecture, and by
using FastText Embedding to retrieve similarity between documents, we compare
methods with large text datasets and ground truth evaluation of clustering
capacities. The datasets used were Reuters and COVID-19 emotions. The results
provide a new view for the contextualization of similarity when approaching
flux of documents organization tasks, based on the similarity between documents
in the flux, and by using mentioned algorithms.

本论文通过使用增量算法如 Incremental TextRank 和 IS-TFIDF 来探索文本文档流，并构建了一种文本文档流组织的体系结构，以便更快速地处理大规模文本数据集的聚类能力，并基于文档之间的相似性提供了一种全新的文档组织视角。