Automaic Text Summarization

Automatic summarization is the procedure of decreasing the content of a document with a machine (computer) program so as to make a summary that holds the most critical sentences of the text file (document). Extracting summary from the document is a difficult task for human beings. Therefore to generate summary automatically has to facilitate several challenges; as the system automates it can only extract the required information from the original document. As the issue of information overload has grown - trouble has been initiated, and as the measure of data has extended, so has eagerness to customize it. It is uncommonly troublesome for individuals to physically condense broad reports of substance. Automatic Summarization systems may be classified into extractive and abstractive summary. An extractive summary method involves selecting indispensable sentences from the record and interfacing them into shorter structure. The vitality of sentences chosen is focused around factual and semantic characteristics of sentences. Extractive method work by selecting a subset of existing words, or sentences in the text file (content document) to produce the summary of input text file. The looking of important data from a huge content document is exceptionally difficult occupation for the user consequently to programmed concentrate the imperative information or summary of the content record. This summary helps the users to reduce time instead Of reading the whole text document and it provide quick knowledge from the large text file. The extractive summarization are commonly focused around techniques for sentence extraction to blanket the set of sentences that are most important for the general understanding of a given text file. In frequency based technique, obtained summary makes more meaning. But in k-means clustering due to out of order extraction, summary might not make sens

Automatic text summarization

Automatic summarization is the procedure of decreasing the content of a document with a machine (computer) program so as to make a summary that holds the most critical sentences of the text file (document). Extracting summary from the document is a difficult task for human beings. Therefore to generate summary automatically has to facilitate several challenges; as the system automates it can only extract the required information from the original document. As the issue of information overload has grown - trouble has been initiated, and as the measure of data has extended, so has eagerness to customize it. It is uncommonly troublesome for individuals to physically condense broad reports of substance. Automatic Summarization systems may be classified into extractive and abstractive summary. An extractive summary method involves selecting indispensable sentences from the record and interfacing them into shorter structure. The vitality of sentences chosen is focused around factual and semantic characteristics of sentences. Extractive method work by selecting a subset of existing words, or sentences in the text file (content document) to produce the summary of input text file. The looking of important data from a huge content document is exceptionally difficult occupation for the user consequently to programmed concentrate the imperative information or summary of the content record. This summary helps the users to reduce time instead Of reading the whole text document and it provide quick knowledge from the large text file. The extractive summarization are commonly focused around techniques for sentence extraction to blanket the set of sentences that are most important for the general understanding of a given text file. In frequency based technique, obtained summary makes more meaning. But in k-means clustering due to out of order extraction, summary might not make sense

Automatic Multiple Document Text Summarization using Wordnet and Agility Tool

The number of web pages on the World Wide Web is increasing very rapidly. Consequently, search engines like Google, AltaVista, Bing etc. provides a long list of URLs to the end user. So, it becomes very difficult to review and analyze each web page manually. That2019;s why automatic text sumarization is used to summarize the source text into its shorter version by preserving its information content and overall meaning. This paper proposes an automatic multiple documents text summarization technique called AMDTSWA, which allows the end user to select multiple URLs to generate their summarized results in parallel. AMDTSWA makes the use of concept based segmentation, HTML DOM tree and concept blocks formation. Similarities of contents are determined by calculating the sentence score and useful information is extracted for generating a comparative summary. The proposed approach is implemented by using ASP.Net and gives good results

Automatic Text Summarization Using Fuzzy Inference

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods

A review of the extractive text summarization

Research in the area of automatic text summarization has intensifed in recent years due to the large amount of information available in electronic documents. This article present the most relevant methods for automatic text extractive summarization that have been developed both for a single document and multiple documents, with special emphasis on methods based on algebraic reduction, clustering and evolutionary models, of which there is great amount of research in recent years, since they are language-independent and unsupervised methods.Las investigaciones en el área de generación automática de resúmenes de textos se han intensifcado en los últimos años debido a la gran cantidad de información disponible en documentos electrónicos. Este artículo presenta los métodos más relevantes de generación automática de resúmenes extractivos que se han desarrollado tanto para un solo documento como para múltiples documentos, haciendo especial énfasis en los métodos basados en reducción algebraica, en agrupamiento y en modelos evolutivos, de los cuales existe gran cantidad de investigaciones en los últimos años, dado que son métodos independientes del lenguaje y no supervisados. &nbsp

A review of the extractive text summarization

Research in the area of automatic text summarization has intensifed in recent years due to the large amount of information available in electronic documents. This article present the most relevant methods for automatic text extractive summarization that have been developed both for a single document and multiple documents, with special emphasis on methods based on algebraic reduction, clustering and evolutionary models, of which there is great amount of research in recent years, since they are language-independent and unsupervised methods.Las investigaciones en el área de generación automática de resúmenes de textos se han intensifcado en los últimos años debido a la gran cantidad de información disponible en documentos electrónicos. Este artículo presenta los métodos más relevantes de generación automática de resúmenes extractivos que se han desarrollado tanto para un solo documento como para múltiples documentos, haciendo especial énfasis en los métodos basados en reducción algebraica, en agrupamiento y en modelos evolutivos, de los cuales existe gran cantidad de investigaciones en los últimos años, dado que son métodos independientes del lenguaje y no supervisados. &nbsp

Hybrid fuzzy multi-objective particle swarm optimization for taxonomy extraction

Ontology learning refers to an automatic extraction of ontology to produce the ontology learning layer cake which consists of five kinds of output: terms, concepts, taxonomy relations, non-taxonomy relations and axioms. Term extraction is a prerequisite for all aspects of ontology learning. It is the automatic mining of complete terms from the input document. Another important part of ontology is taxonomy, or the hierarchy of concepts. It presents a tree view of the ontology and shows the inheritance between subconcepts and superconcepts. In this research, two methods were proposed for improving the performance of the extraction result. The first method uses particle swarm optimization in order to optimize the weights of features. The advantage of particle swarm optimization is that it can calculate and adjust the weight of each feature according to the appropriate value, and here it is used to improve the performance of term and taxonomy extraction. The second method uses a hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems that ensures that the membership functions and fuzzy system rule sets are optimized. The advantage of using a fuzzy system is that the imprecise and uncertain values of feature weights can be tolerated during the extraction process. This method is used to improve the performance of taxonomy extraction. In the term extraction experiment, five extracted features were used for each term from the document. These features were represented by feature vectors consisting of domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. For taxonomy extraction, matching Hearst lexico-syntactic patterns in documents and the web, and hypernym information form WordNet were used as the features that represent each pair of terms from the texts. These two proposed methods are evaluated using a dataset that contains documents about tourism. For term extraction, the proposed method is compared with benchmark algorithms such as Term Frequency Inverse Document Frequency, Weirdness, Glossary Extraction and Term Extractor, using the precision performance evaluation measurement. For taxonomy extraction, the proposed methods are compared with benchmark methods of Feature-based and weighting by Support Vector Machine using the f-measure, precision and recall performance evaluation measurements. For the first method, the experiment results concluded that implementing particle swarm optimization in order to optimize the feature weights in terms and taxonomy extraction leads to improved accuracy of extraction result compared to the benchmark algorithms. For the second method, the results concluded that the hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems leads to improved performance of taxonomy extraction results when compared to the benchmark methods, while adjusting the fuzzy membership function and keeping the number of fuzzy rules to a minimum number with a high degree of accuracy