René Arnulfo García-Hernández

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Automatic text summarization helps the user to quickly understand large volumes of information. We present a language-and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that(More)
In this paper, two algorithms for discovering all the Maximal Sequential Patterns (MSP) in a document collection and in a single document are presented. The proposed algorithms follow the " pattern-growth strategy " where small frequent sequences are found first with the goal of growing them to obtain MSP. Our algorithms process the documents in an(More)
Automatic text summarization has emerged as a technique for accessing only to useful information. In order to known the quality of the automatic summaries produced by a system, in DUC 2002 (Document Understanding Conference) has developed a standard human summaries called gold collection of 567 documents of single news. In this conference only five systems(More)
The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. Although , some approaches claim being domain and language independent, they use high dependence knowledge like key-phrases or golden samples for machine-learning approaches. In this work, we propose a language-and(More)
—The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. For such purpose, recently some approaches have successfully employed the word sequence information from the self-text for detecting the candidate text fragments for composing the summary. In this paper, we employ the(More)
Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of(More)
We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a(More)