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Дисертації з теми "SUMMARIZATION ALGORITHMS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 31 липня 2025

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1

Kolla, Maheedhar, and University of Lethbridge Faculty of Arts and Science. "Automatic text summarization using lexical chains : algorithms and experiments." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2004, 2004. http://hdl.handle.net/10133/226.

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Анотація:

Summarization is a complex task that requires understanding of the document content to determine the importance of the text. Lexical cohesion is a method to identify connected portions of the text based on the relations between the words in the text. Lexical cohesive relations can be represented using lexical chaings. Lexical chains are sequences of semantically related words spread over the entire text. Lexical chains are used in variety of Natural Language Processing (NLP) and Information Retrieval (IR) applications. In current thesis, we propose a lexical chaining method that includes the g

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2

Hodulik, George M. "Graph Summarization: Algorithms, Trained Heuristics, and Practical Storage Application." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1482143946391013.

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3

Hamid, Fahmida. "Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862796/.

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Анотація:

Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with min

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4

Chiarandini, Luca. "Characterizing and modeling web sessions with applications." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283414.

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Анотація:

This thesis focuses on the analysis and modeling of web sessions, groups of requests made by a single user for a single navigation purpose. Understanding how people browse through websites is important, helping us to improve interfaces and provide to better content. After first conducting a statistical analysis of web sessions, we go on to present algorithms to summarize and model web sessions. Finally, we describe applications that use novel browsing methods, in particular parallel browsing. We observe that people tend to browse images in a sequences and that those sequences can be co

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5

Santos, Joelson Antonio dos. "Algoritmos rápidos para estimativas de densidade hierárquicas e suas aplicações em mineração de dados." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25102018-174244/.

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Анотація:

O agrupamento de dados (ou do inglês Clustering) é uma tarefa não supervisionada capaz de descrever objetos em grupos (ou clusters), de maneira que objetos de um mesmo grupo sejam mais semelhantes entre si do que objetos de grupos distintos. As técnicas de agrupamento de dados são divididas em duas principais categorias: particionais e hierárquicas. As técnicas particionais dividem um conjunto de dados em um determinado número de grupos distintos, enquanto as técnicas hierárquicas fornecem uma sequência aninhada de agrupamentos particionais separados por diferentes níveis de granularidade. Adi

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6

Krübel, Monique. "Analyse und Vergleich von Extraktionsalgorithmen für die Automatische Textzusammenfassung." Master's thesis, Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601180.

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Анотація:

Obwohl schon seit den 50er Jahren auf dem Gebiet der Automatischen Textzusammenfassung Forschung betrieben wird, wurden der Nutzen und die Notwendigkeit dieser Systeme erst mit dem Boom des Internets richtig erkannt. Das World Wide Web stellt eine täglich wachsende Menge an Informationen zu nahezu jedem Thema zur Verfügung. Um den Zeitaufwand zum Finden und auch zum Wiederfinden der richtigen Informationen zu minimieren, traten Suchmaschinen ihren Siegeszug an. Doch um einen Überblick zu einem ausgewählten Thema zu erhalten, ist eine einfache Auflistung aller in Frage kommenden Seiten nicht

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7

Maaloul, Mohamed. "Approche hybride pour le résumé automatique de textes : Application à la langue arabe." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4778.

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Анотація:

Cette thèse s'intègre dans le cadre du traitement automatique du langage naturel. La problématique du résumé automatique de documents arabes qui a été abordée, dans cette thèse, s'est cristallisée autour de deux points. Le premier point concerne les critères utilisés pour décider du contenu essentiel à extraire. Le deuxième point se focalise sur les moyens qui permettent d'exprimer le contenu essentiel extrait sous la forme d'un texte ciblant les besoins potentiels d'un utilisateur. Afin de montrer la faisabilité de notre approche, nous avons développé le système "L.A.E", basé sur une approche

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8

Pokorný, Lubomír. "Metody sumarizace textových dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236443.

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Анотація:

This thesis deals with one-document summarization of text data. Part of it is devoted to data preparation, mainly to the normalization. Listed are some of the stemming algorithms and it contains also description of lemmatization. The main part is devoted to Luhn"s method for summarization and its extension of use WordNet dictionary. Oswald summarization method is described and applied as well. Designed and implemented application performs automatic generation of abstracts using these methods. A set of experiments where developed, which verified correct functionality of the application and of e

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9

Hassanlou, Nasrin. "Probabilistic graph summarization." Thesis, 2012. http://hdl.handle.net/1828/4403.

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Анотація:

We study group-summarization of probabilistic graphs that naturally arise in social networks, semistructured data, and other applications. Our proposed framework groups the nodes and edges of the graph based on a user selected set of node attributes. We present methods to compute useful graph aggregates without the need to create all of the possible graph-instances of the original probabilistic graph. Also, we present an algorithm for graph summarization based on pure relational (SQL) technology. We analyze our algorithm and practically evaluate its efficiency using an extended Epinions

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10

SINGH, SWATI. "ANALYSIS FOR TEXT SUMMARIZATION ALGORITHMS FOR DIFFERENT DATASETS." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15975.

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Анотація:

With the exponential increase in the data available on the internet for a single domain, it is difficult to understand the gist of a whole document without reading the whole document. Automatic Text Summarization reduces the content of the document by presenting important key points from the data. Extracting the major points from the document is easier and requires less machinery than forming new sentences from the available data. Research in this domain started nearly 50 years ago from identifying key features to rank important sentences in a text document. The main aim of text summariz

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11

Chen, Chun-Chang, and 陳俊章. "An Ensemble Approach for Multi-document Summarization using Genetic Algorithms." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/26z56t.

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Анотація:

碩士<br>元智大學<br>資訊工程學系<br>106<br>Multi-document summarization is an important research task in text summarization. It helps people to reduce much time in reading articles of the same topics but with similar contents. In this study, we propose an ensemble model based on genetic algorithms. Using this model, we construct two ensemble summarization models, one for four network summarization models, and the other for four probabilistic topic network models. These two ensemble models use genetic algorithms to find the optimal weights. We use the datasets of DUC 2004 to DUC 2007 for performance evalua

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12

Chen, Yong-Jhih, and 陳泳志. "A Text Summarization Model based on Genetic Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/40844191853732361706.

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13

Tatarko, William. "Sumarizace větvených cyklů." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448637.

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Анотація:

In this thesis we present a novel algorithm for summarization of loops with multiple branches operating over integers. The algorithm is based on anal- ysis of a so-called state diagram, which reflects feasibility of various branch interleavings. Summarization can be used to replace loops with equivalent non-iterative statements. This supports examination of reachability and can be used for software verification. For instance, summarization may also be used for (compiler) optimizations. 1

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14

CHEN, YU-HSUAN, and 陳玉軒. "Constructing the Multi-Topics and Multi-Document Summarization Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qn8r6c.

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Анотація:

碩士<br>輔仁大學<br>統計資訊學系應用統計碩士在職專班<br>106<br>Document summarization is a very important topic in text mining. In the past, most researches focused on single or multi-document summarization in specific events or topics. However, there has been no summarization research focus on multi-documents in multi-topics in the same time. In this study, the proposed algorithm can cluster the news by analyzing the similarities among multiple news from 736 news stories in 9 different topics, and the clustering accuracy is 0.66. The ROUGE-N score of the proposed algorithm is not only better than TextRank and LexR

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15

AGGARWAL, TUSHAR. "IMAGE DESCRIPTIVE SUMMARIZATION BY DEEP LEARNING AND ADVANCED LSTM MODEL ARCHITECTURE." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17084.

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Анотація:

Auto Image Descriptor is becoming a trending point of interest in current era of research among researchers. Being a great community, which is proposing a continuous and enhanced Iist of intuitive algorithms which is solving to its problems. However, still there are lot of improvement to this field. Therefore, it’s becoming a field of attraction for many researchers and industries and reliable to this digital world. Of these various image descriptive algorithms, some outperform others in terms of basic descriptors requirements like robustness, invisibility, processing cost, etc.

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16

Chester, Sean. "Representative Subsets for Preference Queries." Thesis, 2013. http://hdl.handle.net/1828/4833.

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Анотація:

We focus on the two overlapping areas of preference queries and dataset summarization. A (linear) preference query specifies the relative importance of the attributes in a dataset and asks for the tuples that best match those preferences. Dataset summarization is the task of representing an entire dataset by a small, representative subset. Within these areas, we focus on three important sub-problems, significantly advancing the state-of-the-art in each. We begin with an investigation into a new formulation of preference queries, identifying a neglected and important subclass that we call thre

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17

TANG, ZHUO-YUE, and 唐卓悅. "Combining Main Path Analysis and Citation Analysis to Construct an Automatic Summarization Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/uckqxq.

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Анотація:

碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>107<br>As we know, the process of learning or anything else is continuous and evolving, and so is the advance of academic papers. In the past, most researches focused on analysis of citation frequency and content. This study focuses on main path analysis, citation analysis and text mining techniques so that this study can apply the approach to make an automatic summary of a single topic. Meanwhile, the researchers can obtain the most complete abstract citation by summarizing the required quotation and its main content automatically according to the relevance of t

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