Automatic text summarization with Maximal Frequent Sequences

En las últimas dos décadas un aumento exponencial de la información electrónica ha provocado una gran necesidad de entender rápidamente grandes volúmenes de información. En este libro se desarrollan los métodos automáticos para producir un resumen. Un resumen es un texto corto que transmite la información más importante de un documento o de una colección de documentos. Los resúmenes utilizados en este libro son extractivos: una selección de las oraciones más importantes del texto. Otros retos consisten en generar resúmenes de manera independiente de lenguaje y dominio. Se describe la identificación de cuatro etapas para generación de resúmenes extractivos. La primera etapa es la selección de términos, en la que uno tiene que decidir qué unidades contarían como términos individuales. El proceso de estimación de la utilidad de los términos individuales se llama etapa de pesado de términos. El siguiente paso se denota como pesado de oraciones, donde todas las secuencias reciben alguna medida numérica de acuerdo con la utilidad de términos. Finalmente, el proceso de selección de las oraciones más importantes se llama selección de oraciones. Los diferentes métodos para generación de resúmenes extractivos pueden ser caracterizados como representan estas etapas. En este libro se describe la etapa de selección de términos, en la que la detección de descripciones multipalabra se realiza considerando Secuencias Frecuentes Maximales (sfms), las cuales adquieren un significado importante, mientras Secuencias Frecuentes (sf) no maximales, que son partes de otros sf, no deben de ser consideradas. En la motivación se consideró costo vs. beneficio: existen muchas sf no maximales, mientras que la probabilidad de adquirir un significado importante es baja. De todos modos, las sfms representan todas las sfs en el modo compacto: todas las sfs podrían ser obtenidas a partir de todas las sfms explotando cada sfm al conjunto de todas sus subsecuencias. Se presentan los nuevos métodos basados en grafos, algoritmos de agrupamiento y algoritmos genéticos, los cuales facilitan la tarea de generación de resúmenes de textos. Se ha experimentado diferentes combinaciones de las opciones de selección de términos, pesado de términos, pesado de oraciones y selección de oraciones para generar los resúmenes extractivos de textos independientes de lenguaje y dominio para una colección de noticias. Se ha analizado algunas opciones basadas en descripciones multipalabra considerándolas en los métodos de grafos, algoritmos de agrupamiento y algoritmos genéticos. Se han obtenido los resultados superiores al de estado de arte. Este libro está dirigido a los estudiantes y científicos del área de Lingüística Computacional, y también a quienes quieren saber sobre los recientes avances en las investigaciones de generación automática de resúmenes de textos.In the last two decades, an exponential increase in the available electronic information causes a big necessity to quickly understand large volumes of information. It raises the importance of the development of automatic methods for detecting the most relevant content of a document in order to produce a shorter text. Automatic Text Summarization (ats) is an active research area dedicated to generate abstractive and extractive summaries not only for a single document, but also for a collection of documents. Other necessity consists in finding method for ats in a language and domain independent way. In this book we consider extractive text summarization for single document task. We have identified that a typical extractive summarization method consists in four steps. First step is a term selection where one should decide what units will count as individual terms. The process of estimating the usefulness of the individual terms is called term weighting step. The next step denotes as sentence weighting where all the sentences receive some numerical measure according to the usefulness of its terms. Finally, the process of selecting the most relevant sentences calls sentence selection. Different extractive summarization methods can be characterized how they perform these steps. In this book, in the term selection step, we describe how to detect multiword descriptions considering Maximal Frequent Sequences (mfss), which bearing important meaning, while non-maximal frequent sequences (fss), those that are parts of another fs, should not be considered. Our additional motivation was cost vs. benefit considerations: there are too many non-maximal fss while their probability to bear important meaning is lower. In any case, mfss represent all fss in a compact way: all fss can be obtained from all mfss by bursting each mfs into a set of all its subsequences.New methods based on graph algorithms, genetic algorithms, and clustering algorithms which facilitate the text summarization task are presented. We have tested different combinations of term selection, term weighting, sentence weighting and sentence selection options for language-and domain-independent extractive single-document text summarization on a news report collection. We analyzed several options based on mfss, considering them with graph, genetic, and clustering algorithms. We obtained results superior to the existing state-ofthe- art methods. This book is addressed for students and scientists of the area of Computational Linguistics, and also who wants to know recent developments in the area of Automatic Text Generation of Summaries

Automatic Generation of Text Summaries - Challenges, proposals and experiments

Los estudiantes e investigadores en el área de procesamiento deenguaje natural, inteligencia artificial, ciencias computacionales y lingüística computacional serán quizá los primeros interesados en este libro. No obstante, también se pretende introducir a público no especializado en esta prometedora área de investigación; por ello, hemos traducido al español algunos tecnicismos y anglicismos, propios de esta disciplina, pero sin dejar de mencionar, en todo momento, su término en inglés para evitar confusiones y lograr que aquellos lectores interesados puedan ampliar sus fuentes de conocimiento.Este libro presenta un método computacional novedoso, a nivel internacional, para la generación automática de resúmenes de texto, pues supera la calidad de los que actualmente se pueden crear. Es decir, es resultado de una investigación que buscó métodos y modelos computacionales lo menos dependientes del lenguaje y dominio

Reglas que describen la deserción y permanencia en los estudiantes de la UAP Tianguistenco de la UAEM

Se pretende encontrar cuál es el conjunto de reglas de conocimiento que pueden extraerse de aquellos estudiantes que han desertado o que permanecen en sus estudios universitarios tres años después de su ingreso. Se utilizó una base de datos inicial con 206 factores y 305 estudiantes de cuatro licenciaturas de la uap Tianguistenco de la uaem . Mediante árboles de decisión, fue posible determinar que con sólo 12 factores en 19 reglas se puede saber, con un 82.6% de soporte, si un estudiante tiene riesgo de desertar o no de sus estudios en los tres años posteriores

Evolutionary Automatic Text Summarization using Cluster Validation Indexes

The main problem for generating an extractive automatic text summary (EATS) is to detect the key themes of a text. For this task, unsupervised approaches cluster the sentences of the original text to find the key sentences that take part in an automatic summary. The quality of an automatic summary is evaluated using similarity metrics with human-made summaries. However, the relationship between the quality of the human-made summaries and the internal quality of the clustering is unclear. First, this paper proposes a comparison of the correlation of the quality of a human-made summary to the internal quality of the clustering validation index for finding the best correlation with a clustering validation index. Second, in this paper, an evolutionary method based on the best above internal clustering validation index for an automatic text summarization task is proposed. Our proposed unsupervised method for EATS has the advantage of not requiring information regarding the specific classes or themes of a text, and is therefore domain- and language-independent. The high results obtained by our method, using the most-competitive standard collection for EATS, prove that our method maintains a high correlation with human-made summaries, meeting the specific features of the groups, for example, compaction, separation, distribution, and density

Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms

Over the last years, several Multi-Document Summarization (MDS) methods have been presented in Document Understanding Conference (DUC), workshops. Since DUC01, several methods have been presented in approximately 268 publications of the stateof-the-art, that have allowed the continuous improvement of MDS, however in most works the upper bounds were unknowns. Recently, some works have been focused to calculate the best sentence combinations of a set of documents and in previous works we have been calculated the significance for single-document summarization task in DUC01 and DUC02 datasets. However, for MDS task has not performed an analysis of significance to rank the best multi-document summarization methods. In this paper, we describe a Genetic Algorithm-based method for calculating the best sentence combinations of DUC01 and DUC02 datasets in MDS through a Meta-document representation. Moreover, we have calculated three heuristics mentioned in several works of state-of-the-art to rank the most recent MDS methods, through the calculus of upper bounds and lower bounds

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

The automatic text summarization (ATS) task consists in automatically synthesizing a document to provide a condensed version of it. Creating a summary requires not only selecting the main topics of the sentences but also identifying the key relationships between these topics. Related works rank text units (mainly sentences) to select those that could form the summary. However, the resulting summaries may not include all the topics covered in the source text because important information may have been discarded. In addition, the semantic structure of documents has been barely explored in this field. Thus, this study proposes a new method for the ATS task that takes advantage of semantic information to improve keyword detection. This proposed method increases not only the coverage by clustering the sentences to identify the main topics in the source document but also the precision by detecting the keywords in the clusters. The experimental results of this work indicate that the proposed method outperformed previous methods with a standard collection

The Impact of Key Ideas on Automatic Deception Detection in Text

In recent years, with the rise of the Internet, the automatic deception detection in text is an important task to recognize those of documents that try to make people believe in something false. Current studies in this field assume that the entire document contains cues to identify deception; however, as demonstrated in this work, some irrelevant ideas in text could affect the performance of the classification. Therefore, this research proposes an approach for deception detection in text that identifies, in the first instance, key ideas in a document based on a topic modeling algorithm and a proposed automatic extractive text summarization method, to produce a synthesized document that avoids secondary ideas. The experimental results of this study indicate that the proposed method outperform previous methods with standard collections

Calculating the Upper Bounds for Portuguese Automatic Text Summarization Using Genetic Algorithm

Over the last years, Automatic Text Summarization (ATS) has been considered as one of the main tasks in Natural Language Processing (NLP) that generates summaries in several languages (e.g., English, Portuguese, Spanish, etc.). One of the most significant advances in ATS is developed for Portuguese reflected with the proposals of various state-of-art methods. It is essential to know the performance of different state-of-the-art methods with respect to the upper bounds (Topline), lower bounds (Baseline-random), and other heuristics (Base-line-first). In recent works, the significance and upper bounds for Single-Docu-ment Summarization (SDS) and Multi-Document Summarization (MDS) using corpora from Document Understanding Conferences (DUC) were calculated. In this paper, a calculus of upper bounds for SDS in Portuguese using Genetic Al-gorithms (GA) is performed. Moreover, we present a comparison of some state-of-the-art methods with respect to the upper bounds, lower bounds, and heuristics to determinate their level of significance

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

The textual information has accelerated growth in the most spoken languages by native Internet users, such as Chinese, Spanish, English, Arabic, Hindi, Portuguese, Bengali, Russian, among others. It is necessary to innovate the methods of Automatic Text Summarization (ATS) that can extract essential information without reading the entire text. The most competent methods are Extractive ATS (EATS) that extract essential parts of the document (sentences, phrases, or paragraphs) to compose a summary. During the last 60 years of research of EATS, the creation of standard corpus with human-generated summaries and evaluation methods which are highly correlated with human judgments help to increase the number of new state-of-the-art methods. However, these methods are mainly supported for the English language, leaving aside other equally important languages such as Spanish, which is the second most spoken language by natives and the third most used on the Internet. A standard corpus for Spanish EATS (SAETS) is created to evaluate the state-of-the-art methods and systems for the Spanish language. The main contribution consists of a proposal for configuration and evaluation of 5 state-ofthe-art methods, five systems and four heuristics using three evaluation methods (ROUGE, ROUGE-C, and Jensen-Shannon divergence). It is the first time that Jensen-Shannon divergence is used to evaluate AETS. In this paper the ground truth bounds for the Spanish language are presented, which are the heuristics baseline:first, baseline:random, topline and concordance. In addition, the ranking of 30 evaluation tests of the state-of-the-art methods and systems is calculated that forms a benchmark for SAETS