Relevant bibliographies by topics / LM. Automatic text retrieval

Academic literature on the topic 'LM. Automatic text retrieval'

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Published: 10 March 2023

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Journal articles on the topic "LM. Automatic text retrieval"

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SALTON, G. "Developments in Automatic Text Retrieval." Science 253, no. 5023 (August 30, 1991): 974–80. http://dx.doi.org/10.1126/science.253.5023.974.

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Wai Lam, M. Ruiz, and P. Srinivasan. "Automatic text categorization and its application to text retrieval." IEEE Transactions on Knowledge and Data Engineering 11, no. 6 (1999): 865–79. http://dx.doi.org/10.1109/69.824599.

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Salton, Gerard. "Another look at automatic text-retrieval systems." Communications of the ACM 29, no. 7 (July 1986): 648–56. http://dx.doi.org/10.1145/6138.6149.

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Foo, Schubert, Siu Cheung Hui, Hong Koon Lim, and Li Hui. "Automatic thesaurus for enhanced Chinese text retrieval." Library Review 49, no. 5 (July 2000): 230–40. http://dx.doi.org/10.1108/00242530010331754.

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Salton, Gerard, and Christopher Buckley. "Term-weighting approaches in automatic text retrieval." Information Processing & Management 24, no. 5 (January 1988): 513–23. http://dx.doi.org/10.1016/0306-4573(88)90021-0.

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Heinrich, Helen, and Eric Willis. "Automated storage and retrieval system: a time-tested innovation." Library Management 35, no. 6/7 (August 5, 2014): 444–53. http://dx.doi.org/10.1108/lm-09-2013-0086.

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Purpose – The purpose of this paper is to examine the ongoing life cycle of the world's first library Automated Storage and Retrieval System (ASRS) at the Oviatt Library at the California State University, Northridge (CSUN). Born from the pilot project at the California State University Chancellor's Office, CSUN's ASRS was inaugurated in 1991 and cost over $2,000,000 to implement. It survived a devastating 6.8 Northridge earthquake and protected the collection housed within. Almost 20 years later the CSUN ASRS underwent a major renovation of hardware. With the changing concept of library as space and the construction of Learning Commons at the Oviatt, the demand for ASRS capacity is higher than ever. Design/methodology/approach – In addition to the history and overview, the paper explores the major aspects of ASRS administration: specifications of storage layout and arrangement of the materials, collection policy for storing materials, communication of retrieval requests and ASRS interface and compatibility with successive Integrated Library Systems. Findings – The first ASRS served as proof of concept that a library collection does not lose its effectiveness when low-circulating materials are removed from the open stacks. Furthermore, with the changing concept of library as space and the construction of Learning Commons at the Oviatt, the provision of the nimble, just-in-time collection becomes paramount, and the demand for ASRS increases exponentially. Practical implications – Administrators and librarians who consider investing in ASRS will learn about the principles of storage organization, imperatives and challenges of its conception and long-term management on the example of CSUN. Originality/value – The paper carries unique qualities as it describes the formation and evolution of the world's first library ASRS. The visionary undertaking not only withstood the test of time and nature, it continues to play a pivotal role in Oviatt Library's adaption to the new generation of users’ demands and expectations.
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Salton, Gerard, James Allan, and Chris Buckley. "Automatic structuring and retrieval of large text files." Communications of the ACM 37, no. 2 (February 1994): 97–108. http://dx.doi.org/10.1145/175235.175243.

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Wu, Zimin, and Gwyneth Tseng. "ACTS: An automatic Chinese text segmentation system for full text retrieval." Journal of the American Society for Information Science 46, no. 2 (March 1995): 83–96. http://dx.doi.org/10.1002/(sici)1097-4571(199503)46:2<83::aid-asi2>3.0.co;2-0.

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Hamdy, Abeer, and Mohamed Elsayed. "Automatic Recommendation of Software Design Patterns: Text Retrieval Approach." Journal of Software 13, no. 4 (April 2018): 260–68. http://dx.doi.org/10.17706/jsw.13.4.260-268.

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Kim, Jin-Suk, Du-Seok Jin, Kwang-Young Kim, and Ho-Seop Choe. "Automatic In-Text Keyword Tagging based on Information Retrieval." Journal of Information Processing Systems 5, no. 3 (September 30, 2009): 159–66. http://dx.doi.org/10.3745/jips.2009.5.3.159.

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Dissertations / Theses on the topic "LM. Automatic text retrieval"

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Viana, Hugo Henrique Amorim. "Automatic information retrieval through text-mining." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11308.

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The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
Nowadays, around a huge amount of firms in the European Union catalogued as Small and Medium Enterprises (SMEs), employ almost a great portion of the active workforce in Europe. Nonetheless, SMEs cannot afford implementing neither methods nor tools to systematically adapt innovation as a part of their business process. Innovation is the engine to be competitive in the globalized environment, especially in the current socio-economic situation. This thesis provides a platform that when integrated with ExtremeFactories(EF) project, aids SMEs to become more competitive by means of monitoring schedule functionality. In this thesis a text-mining platform that possesses the ability to schedule a gathering information through keywords is presented. In order to develop the platform, several choices concerning the implementation have been made, in the sense that one of them requires particular emphasis is the framework, Apache Lucene Core 2 by supplying an efficient text-mining tool and it is highly used for the purpose of the thesis.
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Lee, Hyo Sook. "Automatic text processing for Korean language free text retrieval." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322916.

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Kay, Roderick Neil. "Text analysis, summarising and retrieval." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360435.

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Goyal, Pawan. "Analytic knowledge discovery techniques for ad-hoc information retrieval and automatic text summarization." Thesis, Ulster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543897.

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Information retrieval is broadly concerned with the problem of automated searching for information within some document repository to support various information requests by users. The traditional retrieval frameworks work on the simplistic assumptions of “word independence” and “bag-of-words”, giving rise to problems such as “term mismatch” and “context independent document indexing”. Automatic text summarization systems, which use the same paradigm as that of information retrieval, also suffer from these problems. The concept of “semantic relevance” has also not been formulated in the existing literature. This thesis presents a detailed investigation of the knowledge discovery models and proposes new approaches to address these issues. The traditional retrieval frameworks do not succeed in defining the document content fully because they do not process the concepts in the documents; only the words are processed. To address this issue, a document retrieval model has been proposed using concept hierarchies, learnt automatically from a corpora. A novel approach to give a meaningful representation to the concept nodes in a learnt hierarchy has been proposed using a fuzzy logic based soft least upper bound method. A novel approach of adapting the vector space model with dependency parse relations for information retrieval also has been developed. A user query for information retrieval (IR) applications may not contain the most appropriate terms (words) as actually intended by the user. This is usually referred to as the term mismatch problem and is a crucial research issue in IR. To address this issue, a theoretical framework for Query Representation (QR) has been developed through a comprehensive theoretical analysis of a parametric query vector. A lexical association function has been derived analytically using the relevance criteria. The proposed QR model expands the user query using this association function. A novel term association metric has been derived using the Bernoulli model of randomness. x The derived metric has been used to develop a Bernoulli Query Expansion (BQE) model. The Bernoulli model of randomness has also been extended to the pseudo relevance feedback problem by proposing a Bernoulli Pseudo Relevance (BPR) model. In the traditional retrieval frameworks, the context in which a term occurs is mostly overlooked in assigning its indexing weight. This results in context independent document indexing. To address this issue, a novel Neighborhood Based Document Smoothing (NBDS) model has been proposed, which uses the lexical association between terms to provide a context sensitive indexing weight to the document terms, i.e. the term weights are redistributed based on the lexical association with the context words. To address the “context independent document indexing” for sentence extraction based text summarization task, a lexical association measure derived using the Bernoulli model of randomness has been used. A new approach using the lexical association between terms has been proposed to give a context sensitive weight to the document terms and these weights have been used for the sentence extraction task. Developed analytically, the proposed QR, BQE, BPR and NBDS models provide a proper mathematical framework for query expansion and document smoothing techniques, which have largely been heuristic in the existing literature. Being developed in the generalized retrieval framework, as also proposed in this thesis, these models are applicable to all of the retrieval frameworks. These models have been empirically evaluated over the benchmark TREC datasets and have been shown to provide significantly better performance than the baseline retrieval frameworks to a large degree, without adding significant computational or storage burden. The Bernoulli model applied to the sentence extraction task has also been shown to enhance the performance of the baseline text summarization systems over the benchmark DUC datasets. The theoretical foundations alongwith the empirical results verify that the proposed knowledge discovery models in this thesis advance the state of the art in the field of information retrieval and automatic text summarization.
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McMurtry, William F. "Information Retrieval for Call Center Quality Assurance." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587036885211228.

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Brucato, Matteo. "Temporal Information Retrieval." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5690/.

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Ermakova, Liana. "Short text contextualization in information retrieval : application to tweet contextualization and automatic query expansion." Thesis, Toulouse 2, 2016. http://www.theses.fr/2016TOU20023/document.

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La communication efficace a tendance à suivre la loi du moindre effort. Selon ce principe, en utilisant une langue donnée les interlocuteurs ne veulent pas travailler plus que nécessaire pour être compris. Ce fait mène à la compression extrême de textes surtout dans la communication électronique, comme dans les microblogues, SMS, ou les requêtes dans les moteurs de recherche. Cependant souvent ces textes ne sont pas auto-suffisants car pour les comprendre, il est nécessaire d’avoir des connaissances sur la terminologie, les entités nommées ou les faits liés. Ainsi, la tâche principale de la recherche présentée dans ce mémoire de thèse de doctorat est de fournir le contexte d’un texte court à l’utilisateur ou au système comme à un moteur de recherche par exemple.Le premier objectif de notre travail est d'aider l’utilisateur à mieux comprendre un message court par l’extraction du contexte d’une source externe comme le Web ou la Wikipédia au moyen de résumés construits automatiquement. Pour cela nous proposons une approche pour le résumé automatique de documents multiples et nous l’appliquons à la contextualisation de messages, notamment à la contextualisation de tweets. La méthode que nous proposons est basée sur la reconnaissance des entités nommées, la pondération des parties du discours et la mesure de la qualité des phrases. Contrairement aux travaux précédents, nous introduisons un algorithme de lissage en fonction du contexte local. Notre approche s’appuie sur la structure thème-rhème des textes. De plus, nous avons développé un algorithme basé sur les graphes pour le ré-ordonnancement des phrases. La méthode a été évaluée à la tâche INEX/CLEF Tweet Contextualization sur une période de 4 ans. La méthode a été également adaptée pour la génération de snippets. Les résultats des évaluations attestent une bonne performance de notre approche
The efficient communication tends to follow the principle of the least effort. According to this principle, using a given language interlocutors do not want to work any harder than necessary to reach understanding. This fact leads to the extreme compression of texts especially in electronic communication, e.g. microblogs, SMS, search queries. However, sometimes these texts are not self-contained and need to be explained since understanding them requires knowledge of terminology, named entities or related facts. The main goal of this research is to provide a context to a user or a system from a textual resource.The first aim of this work is to help a user to better understand a short message by extracting a context from an external source like a text collection, the Web or the Wikipedia by means of text summarization. To this end we developed an approach for automatic multi-document summarization and we applied it to short message contextualization, in particular to tweet contextualization. The proposed method is based on named entity recognition, part-of-speech weighting and sentence quality measuring. In contrast to previous research, we introduced an algorithm for smoothing from the local context. Our approach exploits topic-comment structure of a text. Moreover, we developed a graph-based algorithm for sentence reordering. The method has been evaluated at INEX/CLEF tweet contextualization track. We provide the evaluation results over the 4 years of the track. The method was also adapted to snippet retrieval. The evaluation results indicate good performance of the approach
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Sequeira, José Francisco Rodrigues. "Automatic knowledge base construction from unstructured text." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17910.

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Mestrado em Engenharia de Computadores e Telemática
Taking into account the overwhelming number of biomedical publications being produced, the effort required for a user to efficiently explore those publications in order to establish relationships between a wide range of concepts is staggering. This dissertation presents GRACE, a web-based platform that provides an advanced graphical exploration interface that allows users to traverse the biomedical domain in order to find explicit and latent associations between annotated biomedical concepts belonging to a variety of semantic types (e.g., Genes, Proteins, Disorders, Procedures and Anatomy). The knowledge base utilized is a collection of MEDLINE articles with English abstracts. These annotations are then stored in an efficient data storage that allows for complex queries and high-performance data delivery. Concept relationship are inferred through statistical analysis, applying association measures to annotated terms. These processes grant the graphical interface the ability to create, in real-time, a data visualization in the form of a graph for the exploration of these biomedical concept relationships.
Tendo em conta o crescimento do número de publicações biomédicas a serem produzidas todos os anos, o esforço exigido para que um utilizador consiga, de uma forma eficiente, explorar estas publicações para conseguir estabelecer associações entre um conjunto alargado de conceitos torna esta tarefa exaustiva. Nesta disertação apresentamos uma plataforma web chamada GRACE, que providencia uma interface gráfica de exploração que permite aos utilizadores navegar pelo domínio biomédico em busca de associações explícitas ou latentes entre conceitos biomédicos pertencentes a uma variedade de domínios semânticos (i.e., Genes, Proteínas, Doenças, Procedimentos e Anatomia). A base de conhecimento usada é uma coleção de artigos MEDLINE com resumos escritos na língua inglesa. Estas anotações são armazenadas numa base de dados que permite pesquisas complexas e obtenção de dados com alta performance. As relações entre conceitos são inferidas a partir de análise estatística, aplicando medidas de associações entre os conceitos anotados. Estes processos permitem à interface gráfica criar, em tempo real, uma visualização de dados, na forma de um grafo, para a exploração destas relações entre conceitos do domínio biomédico.
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Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.

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The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.
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Martinez-Alvarez, Miguel. "Knowledge-enhanced text classification : descriptive modelling and new approaches." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/27205.

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The knowledge available to be exploited by text classification and information retrieval systems has significantly changed, both in nature and quantity, in the last years. Nowadays, there are several sources of information that can potentially improve the classification process, and systems should be able to adapt to incorporate multiple sources of available data in different formats. This fact is specially important in environments where the required information changes rapidly, and its utility may be contingent on timely implementation. For these reasons, the importance of adaptability and flexibility in information systems is rapidly growing. Current systems are usually developed for specific scenarios. As a result, significant engineering effort is needed to adapt them when new knowledge appears or there are changes in the information needs. This research investigates the usage of knowledge within text classification from two different perspectives. On one hand, the application of descriptive approaches for the seamless modelling of text classification, focusing on knowledge integration and complex data representation. The main goal is to achieve a scalable and efficient approach for rapid prototyping for Text Classification that can incorporate different sources and types of knowledge, and to minimise the gap between the mathematical definition and the modelling of a solution. On the other hand, the improvement of different steps of the classification process where knowledge exploitation has traditionally not been applied. In particular, this thesis introduces two classification sub-tasks, namely Semi-Automatic Text Classification (SATC) and Document Performance Prediction (DPP), and several methods to address them. SATC focuses on selecting the documents that are more likely to be wrongly assigned by the system to be manually classified, while automatically labelling the rest. Document performance prediction estimates the classification quality that will be achieved for a document, given a classifier. In addition, we also propose a family of evaluation metrics to measure degrees of misclassification, and an adaptive variation of k-NN.
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Books on the topic "LM. Automatic text retrieval"

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Salton, Gerard. Automatic text processing: The transformation, analysis, and retrieval of information by computer. Reading, Mass: Addison-Wesley, 1988.

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Salton, Gerard. Automatic text processing: The transformation, analysis, and retrieval of information by computer. Reading, Mass: Addison-Wesley, 1989.

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Sabourin, Conrad. Computational linguistics in information science: Information retrieval (full-text or conceptual), automatic indexing, text abstraction, content analysis, information extraction, query languages : bibliography. Montréal: Infolingua, 1994.

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Schweighofer, Erich. Legal knowledge representation: Automatic text analysis in public international and European law. The Hague: Kluwer Law International, 1999.

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Ibekwe-SanJuan, Fidelia. Fouille de textes: Méthodes, outils et applications. Paris: Hermès science publications, 2007.

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Nitin, Indurkhya, and Zhang Tong 1971-, eds. Fundamentals of predictive text mining. London: Springer-Verlag, 2010.

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Habernal, Ivan. Text, Speech and Dialogue: 14th International Conference, TSD 2011, Pilsen, Czech Republic, September 1-5, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Indurkhya, Nitin, Tong Zhang, and Sholom M. Weiss. Fundamentals of Predictive Text Mining. Springer London, Limited, 2015.

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Indurkhya, Nitin, Tong Zhang, and Sholom M. Weiss. Fundamentals of Predictive Text Mining. Springer, 2010.

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Fundamentals of Predictive Text Mining. Springer, 2012.

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Book chapters on the topic "LM. Automatic text retrieval"

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Pourvali, Mohsen, Salvatore Orlando, and Mehrad Gharagozloo. "Improving Clustering Quality by Automatic Text Summarization." In Information Retrieval Technology, 292–303. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28940-3_23.

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Li, Jianqiang, Yu Zhao, and Bo Liu. "Fully Automatic Text Categorization by Exploiting WordNet." In Information Retrieval Technology, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_1.

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Fu, Guohong, Kang-Kwong Luke, GuoDong Zhou, and Ruifeng Xu. "Automatic Expansion of Abbreviations in Chinese News Text." In Information Retrieval Technology, 530–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_42.

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Jung, Wooncheol, Youngjoong Ko, and Jungyun Seo. "Automatic Text Summarization Using Two-Step Sentence Extraction." In Information Retrieval Technology, 71–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31871-2_7.

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Wenliang, Chen, Chang Xingzhi, Wang Huizhen, Zhu Jingbo, and Yao Tianshun. "Automatic Word Clustering for Text Categorization Using Global Information." In Information Retrieval Technology, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31871-2_1.

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Agosti, Maristella, Michela Bacchin, Nicola Ferro, and Massimo Melucci. "Improving the Automatic Retrieval of Text Documents." In Advances in Cross-Language Information Retrieval, 279–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45237-9_23.

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Brown, Ralf D., Jaime G. Carbonell, and Yiming Yang. "Automatic dictionary extraction for cross-language information retrieval." In Text, Speech and Language Technology, 275–98. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-017-2535-4_14.

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Vale, Rodrigo F., Berthier A. Ribeiro-Neto, Luciano R. S. de Lima, Alberto H. F. Laender, and Hermes R. F. Junior. "Improving Text Retrieval in Medical Collections Through Automatic Categorization." In String Processing and Information Retrieval, 197–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39984-1_15.

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Shin, Kwangcheol, Sang-Yong Han, and Alexander Gelbukh. "Balancing Manual and Automatic Indexing for Retrieval of Paper Abstracts." In Text, Speech and Dialogue, 203–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30120-2_26.

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Cailliau, Frederik, and Ariane Cavet. "Mining Automatic Speech Transcripts for the Retrieval of Problematic Calls." In Computational Linguistics and Intelligent Text Processing, 83–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37256-8_8.

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Conference papers on the topic "LM. Automatic text retrieval"

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Dwivedi, Sanjay K., and Chandrakala Arya. "Automatic Text Classification in Information retrieval." In the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905191.

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Salton, Gerard, and Chris Buckley. "Automatic text structuring and retrieval-experiments in automatic encyclopedia searching." In the 14th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/122860.122863.

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De la Peña Sarracén, Gretel Liz, and Paolo Rosso. "Automatic Text Summarization based on Betweenness Centrality." In CERI '18: 5th Spanish Conference in Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3230599.3230611.

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Qu, Yan, Gregory Grefenstette, and David A. Evans. "Automatic transliteration for Japanese-to-English text retrieval." In the 26th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860435.860499.

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Volkmer, Timo, and Apostol Natsev. "Exploring Automatic Query Refinement for Text-Based Video Retrieval." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262951.

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Haiduc, Sonia, Gabriele Bavota, Andrian Marcus, Rocco Oliveto, Andrea De Lucia, and Tim Menzies. "Automatic query reformulations for text retrieval in software engineering." In 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013. http://dx.doi.org/10.1109/icse.2013.6606630.

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Arroyo-Fernández, Ignacio, Juan-Manuel Torres-Moreno, Gerardo Sierra, and Luis Adrián Cabrera-Diego. "Automatic Text Summarization by Non-topic Relevance Estimation." In 8th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006053400890100.

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Iyengar, G., D. Petkova, B. Pytlik, P. Virga, P. Duygulu, S. Feng, P. Ircing, et al. "Joint visual-text modeling for automatic retrieval of multimedia documents." In the 13th annual ACM international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101149.1101154.

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9

Duffing, Gérald. "Text-Image Interaction for Image Retrieval and Semi-Automatic Indexing." In 20th Annual BCS-IRSG Colloquium on IR. BCS Learning & Development, 1998. http://dx.doi.org/10.14236/ewic/irsg1998.2.

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Kan, Min-Yen, and Judith L. Klavans. "Using librarian techniques in automatic text summarization for information retrieval." In the second ACM/IEEE-CS joint conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/544220.544227.

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