Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-04-19T18:44:37Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-04-23T16:27:14Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Made available in DSpace on 2018-04-23T16:27:14Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) Previous issue date: 2017 / Considerando a grande quantidade de informa??es textuais dispon?veis atualmente, principalmente na web, est? se tronando cada vez mais dif?cil o acesso e a assimila??o desse conte?do para o usu?rio. Nesse contexto, torna-se necess?rio buscar tarefas capazes de transformar essa grande quantidade de dados em conhecimento ?til e organizado. Uma alternativa para amenizar esse problema, ? reduzir o volume de informa??es dispon?veis a partir da produ??o de resumos dos textos originais, por meio da sumariza??o autom?tica (SA) de textos. A sumariza??o autom?tica de textos consiste na produ??o autom?tica de resumos a partir de um ou mais textos-fonte, de modo que o sum?rio contenha as informa??es mais relevantes deste. A avalia??o de resumos ? uma tarefa importante no campo da sumariza??o autom?tica de texto, a abordagem mais intuitiva ? a avalia??o humana, por?m ? onerosa e improdutiva. Outra alternativa ? a avalia??o autom?tica, alguns avaliadores foram propostos, sendo a mais conhecida e amplamente usada ? a medida ROUGE (Recall-Oriented Understudy for Gisting Evaluation). Um fator limitante na avalia??o da ROUGE ? a utiliza??o do sum?rio humano de refer?ncia, o que implica em uma restri??o do idioma e dom?nio, al?m de requerer um trabalho humano demorado e oneroso. Diante das dificuldades encontradas na avalia??o de sum?rios autom?ticos, o presente trabalho apresenta o modelo Cassiopeia como um novo m?todo de avalia??o. O modelo ? um agrupador de textos hier?rquico, o qual consiste no uso da sumariza??o na etapa do pr?-processamento, onde a qualidade do agrupamento ? influenciada positivamente conforme a qualidade da sumariza??o. As simula??es realizadas neste trabalho mostraram que a avalia??o realizada pelo modelo Cassiopeia ? semelhante a avalia??o realizada pela ferramenta ROUGE. Por outro lado, a utiliza??o do modelo Cassiopeia como avaliador de sum?rios autom?ticos evidenciou algumas vantagens, sendo as principais; a n?o utiliza??o do sum?rio humano no processo de avalia??o, e a independ?ncia do dom?nio e do idioma. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / Considering the large amount of textual information currently available, especially on the web, it is becoming increasingly difficult to access and assimilate this content to the user. In this context, it becomes necessary to search for tasks that can transform this large amount of information into useful and organized knowledge. The solution, or at least an alternative, to moderate this problem is to reduce the volume of information available, from the production of abstracts of the original texts, through automatic summarization (SA) of texts. The Automatic Summarization of texts consists of the automatic production of abstracts from one or more source texts, which the summary must contain the most relevant information of the source text. The evaluation of abstracts is an important task in the field of automatic text summarization, the most intuitive approach is human evaluation, but it is costly and unproductive. Another alternative is the automatic evaluation, some evaluators have been proposed, and the most widely used is the ROUGE (Recall-Oriented Understudy for Gisting Evaluation). A limiting factor in ROUGE's evaluation is the use of the human reference summary, which implies a restriction of language and domain, as well as requiring time-consuming and expensive human work. In view of the difficulties encountered in the evaluation of automatic summaries, this paper presents the Cassiopeia model as a new evaluation method. The model is a hierarchical text grouper, which consists of the use of the summarization in the stage of the pre-processing, where the quality of the grouping is influenced positively according to the quality of the summarization. The simulations performed in this work showed that the evaluations performed by Cassiopeia in comparison to the ROUGE tool are similar. On the other hand, the use of the Cassiopeia model as an automatic summarization evaluator showed some advantages, the main ones are; being the non-use of the human abstract in the evaluation process, and the independent of the domain and the language.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2015-12-02T20:30:23Z No. of bitstreams: 1 VernerRafaelFerreira_DISSERT.pdf: 946070 bytes, checksum: 463e4394425d0cacfd33640123bf72df (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2015-12-09T00:28:17Z (GMT) No. of bitstreams: 1 VernerRafaelFerreira_DISSERT.pdf: 946070 bytes, checksum: 463e4394425d0cacfd33640123bf72df (MD5) / Made available in DSpace on 2015-12-09T00:28:17Z (GMT). No. of bitstreams: 1 VernerRafaelFerreira_DISSERT.pdf: 946070 bytes, checksum: 463e4394425d0cacfd33640123bf72df (MD5) Previous issue date: 2014-08-22 / A presente pesquisa estuda a aplica??o da an?lise morfossint?tica de textos escritos no idioma do portugu?s brasileiro como uma metodologia para a cria??o de resumos autom?ticos de maneira extrativa. A automa??o de resumos, enquanto ?rea vinculada ao processamento de linguagem natural, estuda a maneira como o computador pode, de maneira aut?noma, construir resumos de textos. Dessa maneira, entendemos que passar para o computador, como instru??o, a maneira como uma l?ngua ? estruturada, em nosso caso o portugu?s brasileiro, se apresenta como uma contribui??o necess?ria para muitas ?reas do conhecimento. Nesse estudo, propomos a defini??o de um m?todo de sumariza??o que automaticamente realize a an?lise morfossint?tica de textos e, com isso, construa as suas cadeias sintagm?ticas. Os sintagmas que comp?em as estruturas sint?ticas s?o ent?o utilizados como elementos qualificadores para as senten?as do texto analisado, sendo que a contabiliza??o desses elementos determina se uma senten?a ir? ou n?o compor o resumo a ser gerado. / This research studies the application of syntagmatic analysis of written texts in the language of Brazilian Portuguese as a methodology for the automatic creation of extractive summaries. The automation of abstracts, while linked to the area of natural language processing (PLN) is studying ways the computer can autonomously construct summaries of texts. For this we use as presupposed the idea that switch to the computer the way a language is structured, in our case the Brazilian Portuguese, it will help in the discovery of the most relevant sentences, and consequently build extractive summaries with higher informativeness. In this study, we propose the definition of a summarization method that automatically perform the syntagmatic analysis of texts and through them, to build an automatic summary. The phrases that make up the syntactic structures are then used to analyze the sentences of the text, so the count of these elements determines whether or not a sentence will compose the summary to be generated

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-05-03T18:35:26Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) valdir_junior_cordeiro_rocha.pdf: 3757934 bytes, checksum: 00a2e6ee18188436daa1415ec6a05021 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-05-04T16:22:37Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) valdir_junior_cordeiro_rocha.pdf: 3757934 bytes, checksum: 00a2e6ee18188436daa1415ec6a05021 (MD5) / Made available in DSpace on 2018-05-04T16:22:37Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) valdir_junior_cordeiro_rocha.pdf: 3757934 bytes, checksum: 00a2e6ee18188436daa1415ec6a05021 (MD5) Previous issue date: 2017 / Com a amplia??o do acesso ? internet e a cria??o de ferramentas que possibilitam pessoas a criarem conte?do, a informa??o dispon?vel cresce de forma acelerada. Textos sobre os mais diversos assuntos e autores s?o criados todos os dias. ? imposs?vel absorver a quantidade de informa??o dispon?vel, o que dificulta a escolha da mais adequada para determinado interesse ou p?blico. A sumariza??o autom?tica de textos, al?m de apresentar um texto de forma condensada, pode simplifica-lo, gerando uma alternativa para ganho de tempo e amplia??o do acesso a informa??o contida aos mais diferentes tipos de leitores. Os sumarizadores autom?ticos existentes atualmente na literatura n?o apresentam m?todos de personifica??o dos sum?rios para cada tipo de leitor, e consequentemente geram resultados pouco precisos. Este trabalho tem como objetivo utilizar o sumarizador autom?tico de textos PragmaSUM em textos educacionais com novas t?cnicas de sumariza??o utilizando palavras-chave. A utiliza??o de m?todos de personifica??o do sum?rio com palavras-chave visa aumentar a precis?o e melhorar o desempenho do PragmaSUM e seus sum?rios. Para isto, um corpus formado apenas por artigos cient?ficos da ?rea educacional foi criado para realiza??o de testes e compara??es entre diferentes sumarizadores e m?todos de sumariza??o. O desempenho dos sumarizadores foi medido pelas m?tricas Recall, Precision e F-Measure presentes na ferramenta ROUGE e validados com os testes estat?sticos ANOVA de Friedman e Coeficiente de Concord?ncia de Kendall. Os resultados obtidos apontam uma melhora no desempenho com a utiliza??o de palavras-chave na sumariza??o com o PragmaSUM, indicando a import?ncia na escolha adequada destas palavras-chave para classifica??o do conte?do do texto fonte. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / By expanding access to the internet and creating tools that enable people to create content, available information grows rapidly. Texts on the most diverse subjects and authors are created every day. It is impossible to absorb the amount of information available, which makes it difficult to choose the most appropriate for a particular interest or public. Automatic text summarization, as well as presenting a condensed text, can simplify it, generating an alternative to gain time and increase the access to information contained to the most different types of readers. The automatic summarizers that currently exist in the literature do not present methods of personification of the summaries for each type of reader, and consequently generate results inaccurate. This work aims to use the PragmaSUM automatic text summarizer in educational texts with new summarization techniques using keywords. Using summary keywords impersonation methods is intended to increase accuracy and improve the performance of PragmaSUM and its summaries. For this, a corpus formed only by scientific articles of the educational area was created to carry out tests and comparisons between different summarizers and summarization methods. The performance of the summarizers was measured by the Recall, Precision and F-Measure metrics present in the ROUGE tool and validated with the Friedman ANOVA statistical tests and Kendall's coefficient of agreement. The results obtained indicate an improvement in the performance with the use of keywords in the summarization with PragmaSUM, pointing out importance in the appropriate choice of these keywords for classification of the content of the source text.