Made available in DSpace on 2016-04-26T20:26:11Z (GMT). No. of bitstreams: 1 Flavia de Almeida Montingelli Zanferdini.pdf: 1693603 bytes, checksum: ad7ab282db3c6e41e17d187968ccc60d (MD5) Previous issue date: 2007-11-11 / The work analyzes the strong and growing tendency of summarization of the contemporary civil process. It starts from the premise that it is necessary to reconcile safety and velocity, being known that this challenge is one that more urges the contemporary procedure experts. To face the multiple subjects that elapse of that tendency, it initially presents the more frequently means used to summarize the procedures. It presents, likewise, the reasons that lead to that growing summarizing tendency, which are the increment of the access to the justice and the increase of the number of actions that are not proportional to the judiciary apparatus. It still discourses about the need to look for mechanisms to accomplish the constitutional warranty of judgement in reasonable term and defends that one cannot, in that search to reconcile velocity and safety, to do without of counterbalance mechanisms, denominated regulatory mechanisms. It exposes the existent regulation mechanisms in the domestic legal system and those used at other countries, suggesting modifications in the national legislation. It objects, with vehemence, to the idea that the velocity search at any cost is an ideal to be pursued. It therefore disagrees of the dominant conceptions that accept that the safety gives up front to the urgency, given the social demands and that understand to be the inherent risk to the need of distribution of the procedural time. It presents a panorama of the provisional remedies in Brazil and in other countries chosen as paradigms − Argentina, Spain, Italy and Portugal − and defends that the provisional remedies should be proportional, or in other words, that they cannot cause the defendant more damages than the benefits that are granted to the author, suggesting therefore some modifications in the law, as well as the strict obedience to the principle of the proportionality in such cases. At the end, it analyzes bills that alter dispositions on the provisional remedies in Argentina, Brasil and Italy, defending, still, a better regulation of the matter in our country / O trabalho analisa a forte e crescente tendência de sumarização do processo civil contemporâneo. Parte da premissa de que é preciso conciliar segurança e celeridade, sabendo-se que esse desafio é um dos que mais instigam os processualistas contemporâneos. Para o enfrentamento das múltiplas questões que decorrem dessa tendência, inicialmente apresenta os meios mais freqüentemente utilizados para sumarizar os procedimentos. Apresenta, outrossim, as razões que levam a essa crescente tendência sumarizante, quais sejam, o incremento do acesso à justiça e o aumento do número de ações, que não são proporcionais ao aparato judiciário. Discorre, ainda, sobre a necessidade de buscar mecanismos para cumprir a garantia constitucional da prestação jurisdicional em prazo razoável e defende que não se pode, nessa busca da conciliação de celeridade e segurança, prescindir de mecanismos de contrapeso, denominados de regulatórios. Expõe os mecanismos de regulação existentes no ordenamento pátrio e aqueles utilizados em outros países, sugerindo modificações na legislação nacional. Contesta, com veemência, a idéia de que a busca de celeridade a qualquer custo seja um ideal a ser perseguido. Discorda, destarte, das concepções dominantes que aceitam que a segurança ceda frente à urgência, dadas as exigências sociais e que entendem ser o risco inerente à necessidade de distribuição do tempo processual. Apresenta um panorama das tutelas de urgência, no Brasil e em outros países escolhidos como paradigmas − Argentina, Espanha, Itália e Portugal − e defende que as tutelas de urgência devam ser proporcionais, ou seja, que não podem causar ao réu mais danos do que os benefícios que são outorgados ao autor, sugerindo, destarte, algumas modificações na lei, bem como a estrita obediência ao princípio da proporcionalidade em tais casos. Ao final, analisa projetos de lei que alteram disposições sobre as tutelas de urgência na Argentina, Brasil e Itália, defendendo, ainda, uma melhor regulamentação da matéria em nosso país

A Sumarização Automática de Textos tem considerável importância nas tarefas de localização e utilização de conteúdo relevante em meio à quantidade enorme de informação disponível atualmente em meio digital. Nessa área, procura-se desenvolver técnicas que possibilitem obter o conteúdo mais relevante de documentos, de maneira condensada, sem alterar seu significado original, e com mínima intervenção humana. O objetivo deste trabalho de mestrado foi investigar de que maneira conceitos desenvolvidos na área de Redes Complexas podem ser aplicados à Sumarização Automática de Textos, mais especificamente à sumarização extrativa. Embora grande parte das pesquisas em sumarização tenha se voltado para a utilização de técnicas extrativas, ainda é possível melhorar o nível de informatividade dos extratos gerados automaticamente. Neste trabalho, textos foram representados como redes, das quais foram extraídas medidas tradicionalmente utilizadas na caracterização de redes complexas (por exemplo, coeficiente de aglomeração, grau hierárquico e índice de localidade), com o intuito de fornecer subsídios à seleção das sentenças mais significativas de um texto. Essas redes são formadas pelas sentenças (representadas pelos vértices) de um determinado texto, juntamente com as repetições (representadas pelas arestas) de substantivos entre sentenças após lematização. Cada método de sumarização proposto foi aplicado no córpus TeMário, de textos jornalísticos em português, e em córpus das conferências DUC, de textos jornalísticos em inglês. A avaliação desse estudo foi feita por meio da realização de quatro experimentos, fazendo-se uso de métodos de avaliação automática (Rouge-1 e Precisão/Cobertura de sentenças) e comparando-se os resultados com os de outros sistemas de sumarização extrativa. Os melhores sumarizadores propostos referem-se aos seguintes conceitos: d-anel, grau, k-núcleo e caminho mínimo. Foram obtidos resultados comparáveis aos dos melhores métodos de sumarização já propostos para o português, enquanto que, para o inglês, os resultados são menos expressivos. / Automatic Text Summarization has considerably importance in tasks such as finding and using relevant content in the enormous amount of information available nowadays in digital media. The focus in this field is on the development of techniques that allow someone to obtain the most relevant content of documents, in a condensed way, preserving the original meaning and with little (or even none) human help. The purpose of this MSc project was to investigate a way of applying concepts borrowed from the studies of Complex Networks to the Automatic Text Summarization field, specifically to the task of extractive summarization. Although the majority of works in summarization have focused on extractive techniques, it is still possible to obtain better levels of informativity in extracts automatically generated. In this work, texts were represented as networks, from which the most significant sentences were selected through the use of ranking algorithms. Such networks are obtained from a text in the following manner: the sentences are represented as nodes, and an edge between two nodes is created if there is at least one repetition of a noun in both sentences, after the lemmatization step. Measurements typically employed in the characterization of complex networks, such as clustering coefficient, hierarchical degree and locality index, were used on the basis of the process of node (sentence) selection in order to build an extract. Each summarization technique proposed was applied to the TeMário corpus, which comprises newspaper articles in Portuguese, and to the DUC corpora, which comprises newspaper articles in English. Four evaluation experiments were carried out, by means of automatic evaluation measurements (Rouge-1 and sentence Precision/Recall) and comparison with the results obtained by other extractive summarization systems. The best summarizers are the ones based on the following concepts: d-ring, degree, k-core and shortest path. Performances comparable to the best summarization systems for Portuguese were achieved, whilst the results are less significant for English.

No description available.

More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions.

Search and summarization of streaming social media, such as Twitter, requires the ongoing analysis of large volumes of data with dynamically changing characteristics. Tweets are short and repetitious -- lacking context and structure -- making it difficult to generate a coherent synopsis of events within a given time period. Although some established algorithms for frequent itemset analysis might provide an efficient foundation for synopsis generation, the unmodified application of standard methods produces a complex mass of rules, dominated by common language constructs and many trivial variations on topically related results. Moreover, these results are not necessarily specific to events within the time period of interest. To address these problems, we build upon the Linear time Closed itemset Mining (LCM) algorithm, which is particularly suited to the large and sparse vocabulary of tweets. LCM generates only closed itemsets, providing an immediate reduction in the number of trivial results. To reduce the impact of function words and common language constructs, we apply a filltering step that preserves these terms only when they may form part of a relevant collocation. To further reduce trivial results, we propose a novel strengthening of the closure condition of LCM to retain only those results that exceed a threshold of distinctiveness. Finally, we perform temporal ranking, based on information gain, to identify results that are particularly relevant to the time period of interest. We evaluate our work over a collection of tweets gathered in late 2012, exploring the efficiency and filtering characteristic of each processing step, both individually and collectively. Based on our experience, the resulting synopses from various time periods provide understandable and meaningful pictures of events within those periods, with potential application to tasks such as temporal summarization and query expansion for search.

No description available.

This thesis addresses difficult challenges in distributed document clustering and cluster summarization. Mining large document collections poses many challenges, one of which is the extraction of topics or summaries from documents for the purpose of interpretation of clustering results. Another important challenge, which is caused by new trends in distributed repositories and peer-to-peer computing, is that document data is becoming more distributed. We introduce a solution for interpreting document clusters using keyphrase extraction from multiple documents simultaneously. We also introduce two solutions for the problem of distributed document clustering in peer-to-peer environments, each satisfying a different goal: maximizing local clustering quality through collaboration, and maximizing global clustering quality through cooperation. The keyphrase extraction algorithm efficiently extracts and scores candidate keyphrases from a document cluster. The algorithm is called CorePhrase and is based on modeling document collections as a graph upon which we can leverage graph mining to extract frequent and significant phrases, which are used to label the clusters. Results show that CorePhrase can extract keyphrases relevant to documents in a cluster with very high accuracy. Although this algorithm can be used to summarize centralized clusters, it is specifically employed within distributed clustering to both boost distributed clustering accuracy, and to provide summaries for distributed clusters. The first method for distributed document clustering is called collaborative peer-to-peer document clustering, which models nodes in a peer-to-peer network as collaborative nodes with the goal of improving the quality of individual local clustering solutions. This is achieved through the exchange of local cluster summaries between peers, followed by recommendation of documents to be merged into remote clusters. Results on large sets of distributed document collections show that: (i) such collaboration technique achieves significant improvement in the final clustering of individual nodes; (ii) networks with larger number of nodes generally achieve greater improvements in clustering after collaboration relative to the initial clustering before collaboration, while on the other hand they tend to achieve lower absolute clustering quality than networks with fewer number of nodes; and (iii) as more overlap of the data is introduced across the nodes, collaboration tends to have little effect on improving clustering quality. The second method for distributed document clustering is called hierarchically-distributed document clustering. Unlike the collaborative model, this model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as a hierarchy of node neighborhoods. Summarization of the global distributed clusters is achieved through a distributed version of the CorePhrase algorithm. Results on large document sets show that: (i) distributed clustering accuracy is not affected by increasing the number of nodes for networks of single level; (ii) we can achieve decent speedup by making the hierarchy taller, but on the expense of clustering quality which degrades as we go up the hierarchy; (iii) in networks that grow arbitrarily, data gets more fragmented across neighborhoods causing poor centroid generation, thus suggesting we should not increase the number of nodes in the network beyond a certain level without increasing the data set size; and (iv) distributed cluster summarization can produce accurate summaries similar to those produced by centralized summarization. The proposed algorithms offer high degree of flexibility, scalability, and interpretability of large distributed document collections. Achieving the same results using current methodologies require centralization of the data first, which is sometimes not feasible.

We propose a method to detect events and event boundaries in soccer videos by using web-casting texts and audio-visual features. The events and their inaccurate time information given in web-casting texts need to be aligned with the visual content of the video. Most match reports presented by popular organizations such as uefa.com (the official site of Union of European Football Associations) provide the time information in minutes rather than seconds. We propose a robust method which is able to handle uncertainties in the time points of the events. As a result of our experiments, we claim that our method detects event boundaries satisfactorily for uncertain web-casting texts, and that the use of audio-visual features improves the performance of event boundary detection.

Search and summarization of streaming social media, such as Twitter, requires the ongoing analysis of large volumes of data with dynamically changing characteristics. Tweets are short and repetitious -- lacking context and structure -- making it difficult to generate a coherent synopsis of events within a given time period. Although some established algorithms for frequent itemset analysis might provide an efficient foundation for synopsis generation, the unmodified application of standard methods produces a complex mass of rules, dominated by common language constructs and many trivial variations on topically related results. Moreover, these results are not necessarily specific to events within the time period of interest. To address these problems, we build upon the Linear time Closed itemset Mining (LCM) algorithm, which is particularly suited to the large and sparse vocabulary of tweets. LCM generates only closed itemsets, providing an immediate reduction in the number of trivial results. To reduce the impact of function words and common language constructs, we apply a filltering step that preserves these terms only when they may form part of a relevant collocation. To further reduce trivial results, we propose a novel strengthening of the closure condition of LCM to retain only those results that exceed a threshold of distinctiveness. Finally, we perform temporal ranking, based on information gain, to identify results that are particularly relevant to the time period of interest. We evaluate our work over a collection of tweets gathered in late 2012, exploring the efficiency and filtering characteristic of each processing step, both individually and collectively. Based on our experience, the resulting synopses from various time periods provide understandable and meaningful pictures of events within those periods, with potential application to tasks such as temporal summarization and query expansion for search.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal