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Dissertations / Theses on the topic 'Multi-document summarization'

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1

Huang, Fang. "Multi-document summarization with latent semantic analysis." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419255.

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2

Ou, Shiyan, Christopher S. G. Khoo, and Dion H. Goh. "Automatic multi-document summarization for digital libraries." School of Communication & Information, Nanyang Technological University, 2006. http://hdl.handle.net/10150/106042.

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With the rapid growth of the World Wide Web and online information services, more and more information is available and accessible online. Automatic summarization is an indispensable solution to reduce the information overload problem. Multi-document summarization is useful to provide an overview of a topic and allow users to zoom in for more details on aspects of interest. This paper reports three types of multi-document summaries generated for a set of research abstracts, using different summarization approaches: a sentence-based summary generated by a MEAD summarization system that extracts
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Grant, Harald. "Extractive Multi-document Summarization of News Articles." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158275.

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Publicly available data grows exponentially through web services and technological advancements. To comprehend large data-streams multi-document summarization (MDS) can be used. In this research, the area of multi-document summarization is investigated. Multiple systems for extractive multi-document summarization are implemented using modern techniques, in the form of the pre-trained BERT language model for word embeddings and sentence classification. This is combined with well proven techniques, in the form of the TextRank ranking algorithm, the Waterfall architecture and anti-redundancy filt
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Geiss, Johanna. "Latent semantic sentence clustering for multi-document summarization." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609761.

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Kipp, Darren. "Shallow semantics for topic-oriented multi-document automatic text summarization." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27772.

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There are presently a number of NLP tools available which can provide semantic information about a sentence. Connexor Machinese Semantics is one of the most elaborate of such tools in terms of the information it provides. It has been hypothesized that semantic analysis of sentences is required in order to make significant improvements in automatic summarization. Elaborate semantic analysis is still not particularly feasible. In this thesis, I will look at what shallow semantic features are available from an off the shelf semantic analysis tool which might improve the responsiveness of a summar
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6

Qumsiyeh, Rani Majed. "Easy to Find: Creating Query-Based Multi-Document Summaries to Enhance Web Search." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2713.

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Current web search engines, such as Google, Yahoo!, and Bing, rank the set of documents S retrieved in response to a user query Q and display each document with a title and a snippet, which serves as an abstract of the corresponding document in S. Snippets, however, are not as useful as they are designed for, i.e., to assist search engine users to quickly identify results of interest, if they exist, without browsing through the documents in S, since they (i) often include very similar information and (ii) do not capture the main content of the corresponding documents. Moreover, when the intend
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7

Aker, Ahmet. "Entity type modeling for multi-document summarization : generating descriptive summaries of geo-located entities." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/5138/.

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In this work we investigate the application of entity type models in extractive multi-document summarization using the automatic caption generation for images of geo-located entities (e.g. Westminster Abbey, Loch Ness, Eiffel Tower) as an application scenario. Entity type models contain sets of patterns aiming to capture the ways the geo-located entities are described in natural language. They are automatically derived from texts about geo-located entities of the same type (e.g. churches, lakes, towers). We collect texts about geo-located entities from Wikipedia because our investigation show
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8

JABEEN, SAIMA. "Document analysis by means of data mining techniques." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537297.

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The huge amount of textual data produced everyday by scientists, journalists and Web users, allows investigating many different aspects of information stored in the published documents. Data mining and information retrieval techniques are exploited to manage and extract information from huge amount of unstructured textual data. Text mining also known as text data mining is the processing of extracting high quality information (focusing relevance, novelty and interestingness) from text by identifying patterns etc. Text mining typically involves the process of structuring input text by means of
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9

Freddi, Davide. "sintesi generativa multi-documento con discriminazione della rilevanza mediante probabilità marginale: una soluzione neurale end-to-end per la letteratura medica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Nonostante lo sviluppo scientifico in molti task di Natural Language Processing (NLP), il task di multi-document summarization rimane meno esplorato, lasciando ampi margini di miglioramento. Nella comunità medica e scientifica, questo task trova applicazione nella generazione delle revisioni sistematiche della letteratura, che sono articoli che riassumono molti studi su uno stesso argomento. In questo lavoro di tesi si esplorano le principali tecnologie in ambito di NLP e text generation, concentrandosi in particolare su BlenderBot 2, un'architettura sviluppata da Facebook AI che rappresenta l
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10

Camargo, Renata Tironi de. "Investigação de estratégias de sumarização humana multidocumento." Universidade Federal de São Carlos, 2013. https://repositorio.ufscar.br/handle/ufscar/5781.

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Made available in DSpace on 2016-06-02T20:25:21Z (GMT). No. of bitstreams: 1 5583.pdf: 2165924 bytes, checksum: 9508776d3397fc5a516393218f88c50f (MD5) Previous issue date: 2013-08-30<br>Universidade Federal de Minas Gerais<br>The multi-document human summarization (MHS), which is the production of a manual summary from a collection of texts from different sources on the same subject, is a little explored linguistic task. Considering the fact that single document summaries comprise information that present recurrent features which are able to reveal summarization strategies, we aimed to inv
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11

Zacarias, Andressa Caroline Inácio. "Investigação de métodos de sumarização automática multidocumento baseados em hierarquias conceituais." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7974.

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Submitted by Livia Mello (liviacmello@yahoo.com.br) on 2016-09-30T19:20:49Z No. of bitstreams: 1 DissACIZ.pdf: 2734710 bytes, checksum: bf061fead4f2a8becfcbedc457a68b25 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T16:19:10Z (GMT) No. of bitstreams: 1 DissACIZ.pdf: 2734710 bytes, checksum: bf061fead4f2a8becfcbedc457a68b25 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T16:19:17Z (GMT) No. of bitstreams: 1 DissACIZ.pdf: 2734710 bytes, checksum: bf061fead4f2a8becfcbedc457a68b25 (MD5)<br>Made availab
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Dias, Márcio de Souza. "Investigação de modelos de coerência local para sumários multidocumento." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11112016-084734/.

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A sumarização multidocumento consiste na tarefa de produzir automaticamente um único sumário a partir de um conjunto de textos derivados de um mesmo assunto. É imprescindível que seja feito o tratamento de fenômenos que ocorrem neste cenário, tais como: (i) a redundância, a complementaridade e a contradição de informações; (ii) a uniformização de estilos de escrita; (iii) tratamento de expressões referenciais; (iv) a manutenção de focos e perspectivas diferentes nos textos; (v) e a ordenação temporal das informações no sumário. O tratamento de tais fenômenos contribui significativamente para q
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Tosta, Fabricio Elder da Silva. "Aplicação de conhecimento léxico-conceitual na sumarização multidocumento multilíngue." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/5796.

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Made available in DSpace on 2016-06-02T20:25:23Z (GMT). No. of bitstreams: 1 6554.pdf: 2657931 bytes, checksum: 11403ad2acdeafd11148154c92757f20 (MD5) Previous issue date: 2014-02-27<br>Financiadora de Estudos e Projetos<br>Traditionally, Multilingual Multi-document Automatic Summarization (MMAS) is a computational application that, from a single collection of source-texts on the same subject/topic in at least two languages, produces an informative and generic summary (extract) in one of these languages. The simplest methods automatically translate the source-texts and, from a monolingual
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14

Linhares, Pontes Elvys. "Compressive Cross-Language Text Summarization." Thesis, Avignon, 2018. http://www.theses.fr/2018AVIG0232/document.

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La popularisation des réseaux sociaux et des documents numériques a rapidement accru l'information disponible sur Internet. Cependant, cette quantité massive de données ne peut pas être analysée manuellement. Parmi les applications existantes du Traitement Automatique du Langage Naturel (TALN), nous nous intéressons dans cette thèse au résumé cross-lingue de texte, autrement dit à la production de résumés dans une langue différente de celle des documents sources. Nous analysons également d'autres tâches du TALN (la représentation des mots, la similarité sémantique ou encore la compression de p
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15

Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202687.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.
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16

Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." BioMed Central, 2015. https://tud.qucosa.de/id/qucosa%3A29496.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.
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17

沈健誠. "Multi-Document Summarization System." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/67547214470615254060.

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碩士<br>國立清華大學<br>資訊工程學系<br>89<br>Most summarization systems are designed for a single document at present. These systems indicate the essence of individual document, but do not transfer similar documents into single summary. Can we develop a multi-document summarization system, which transfers related documents with the same event into a summary? If that is possible, the main points of documents will be clearly and simply displayed with two or three sentences. Users can see whether these documents are what they want in a minute. It can reduce time for collecting documents and enable users to ga
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18

Tsai, Erh-I., and 蔡而益. "Topic-Based Multi-Document Summarization System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/11463643705190386674.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>99<br>With the explosion in the amount of information available electronically, information overloading has become a major problem and people have to spend more and more time to look for the information they need. Automatic text summarization has draw much attention in recent years and it exhibits the practicability in document management and search systems.
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19

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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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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20

Lamkhede, Sudarshan. "Multi-document summarization using concept chain graphs." 2005. http://proquest.umi.com/pqdweb?did=994252731&sid=19&Fmt=2&clientId=39334&RQT=309&VName=PQD.

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Thesis (M.S.)--State University of New York at Buffalo, 2005.<br>Title from PDF title page (viewed on Mar. 16, 2006) Available through UMI ProQuest Digital Dissertations. Thesis adviser: Srihari, Rohini K. Includes bibliographical references.
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21

Alliheedi, Mohammed. "Multi-document Summarization System Using Rhetorical Information." Thesis, 2012. http://hdl.handle.net/10012/6820.

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Over the past 20 years, research in automated text summarization has grown significantly in the field of natural language processing. The massive availability of scientific and technical information on the Internet, including journals, conferences, and news articles has attracted the interest of various groups of researchers working in text summarization. These researchers include linguistics, biologists, database researchers, and information retrieval experts. However, because the information available on the web is ever expanding, reading the sheer volume of information is a significant chal
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22

Wung, Hung_chia, and 翁鴻加. "Some New Approaches to Multi-document Summarization." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/39815575583792443924.

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23

黃思萱. "Multi-Document Summarization Based on Keyword Clustering." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/53800112370126276087.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>90<br>With the rapid growth of the World Wide Web, more and more information is accessible on-line. This explosion of information has resulted in an information overload problem. However, people have no time to read everything and have to decide which information is available. The technology of automatic text summarization is indispensable for dealing with this problem. Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user and task. Recent researches on multi-docume
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Huang, Sheng-Jie, and 黃聖傑. "A Study of a Multi-Document Summarization System." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/92236133128208368289.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>87<br>In the past, there are two approaches for people to get daily news: one is to watch news reports on TV, and the other is to read the newspapers. Recently, due to the popularization of Internet, people can get the news through the news web sites easily. There are many news web sites on the Internet. It is convenient for users to read the daily news in every moment. Because of the different reporters, the reports may be similar or different. If reports of the same event can be combined, the users can spend less time to get more information. The g
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Chen, Yee-ting, and 陳怡婷. "Multi-Document Summarization Based on Event Lexical Chain." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/y4r77g.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>94<br>Due to the population of the internet, people can access much more information easily than before, which leads to information overload problem. In addition, there exists information redundancy problem. For example, a lot of online electronic news websites are built and many websites may present the same events in different arguments. If the user wants to know the overall picture of the thing, they must spend a lot of time to surf various electronic news website and browse related news. One needs to collect and read all related news in order to obtain the comple
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Yang, Ruin-Min, and 楊瑞敏. "Multi-Document Summarization System Based on Mutual Reinforcement Principle." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/59722834005586953270.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>98<br>According to the research report, the rapid development of the Internet results in the amount of the digital document, video, or other data to grow in double rate per year. In order to find out the information of these electronic files efficiently, this thesis develops an automatic summarization system to sieve out the non-information data of digital documents. Therefore, users can find out the contents of information efficiently without losing the meaning of the original documents. The automatic summarization system proposed in this thesis considers three dif
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Su, Tsei-Chun, and 蘇哲君. "A Study of a Chinese-English Multi-Document Summarization System." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/85086128686856139458.

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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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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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Wei, Ling-Yu, and 魏玲玉. "Dynamic Clustering and Multi-Document Summarization Based on the Concept of Document Warehousing — Using Chinese News Articles as Examples." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/24394459454373718346.

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碩士<br>國立高雄第一科技大學<br>資訊管理所<br>92<br>Traditionally, the query results obtained from full-text searching systems based on inverted-index are usually huge in volume and unsorted, which make users suffer from easily determining what information embedded in the collection. Therefore, for contemporary document searching over the Internet, such systems are no longer satisfying user’s need. With the ever growing sea of electronic documents, it is indispensable to provide a mechanism for integrating a large number of documents to facilitate user’s browsing and querying need efficiently. In this pa
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