Relevant bibliographies by topics / Text summarization

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  2. Dissertations / Theses
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Academic literature on the topic 'Text summarization'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Text summarization"

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Sirohi, Neeraj Kumar, Dr Mamta Bansal, and Dr S. N. Rajan Rajan. "Text Summarization Approaches Using Machine Learning & LSTM." Revista Gestão Inovação e Tecnologias 11, no. 4 (September 1, 2021): 5010–26. http://dx.doi.org/10.47059/revistageintec.v11i4.2526.

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Due to the massive amount of online textual data generated in a diversity of social media, web, and other information-centric applications. To select the vital data from the large text, need to study the full article and generate summary also not loose critical information of text document this process is called summarization. Text summarization is done either by human which need expertise in that area, also very tedious and time consuming. second type of summarization is done through system which is known as automatic text summarization which generate summary automatically. There are mainly two categories of Automatic text summarizations that is abstractive and extractive text summarization. Extractive summary is produced by picking important and high rank sentences and word from the text document on the other hand the sentences and word are present in the summary generated through Abstractive method may not present in original text. This article mainly focuses on different ATS (Automatic text summarization) techniques that has been instigated in the present are argue. The paper begin with a concise introduction of automatic text summarization, then closely discussed the innovative developments in extractive and abstractive text summarization methods, and then transfers to literature survey, and it finally sum-up with the proposed techniques using LSTM with encoder Decoder for abstractive text summarization are discussed along with some future work directions.
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D, Manju, Radhamani V, Dhanush Kannan A, Kavya B, Sangavi S, and Swetha Srinivasan. "TEXT SUMMARIZATION." YMER Digital 21, no. 07 (July 7, 2022): 173–82. http://dx.doi.org/10.37896/ymer21.07/13.

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n the last few years, a huge amount of text data from different sources has been created every day. The enormous data which needs to be processed contains valuable detail which needs to be efficiently summarized so that it serves a purpose. It is very tedious to summarize and classify large amounts of documents when done manually. It becomes cumbersome to develop a summary taking every semantics into consideration. Therefore, automatic text summarization acts as a solution. Text summarization can help in understanding the huge corpus by providing a gist of the corpus enabling comprehension in a timely manner. This paper studies the development of a web application which summarizes the given input text using different models and its deployment. Keywords: Text summarization, NLP, AWS, Text mining
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Vikas, A., Pradyumna G.V.N, and Tahir Ahmed Shaik. "Text Summarization." International Journal of Engineering and Computer Science 9, no. 2 (February 3, 2020): 24940–45. http://dx.doi.org/10.18535/ijecs/v9i2.4437.

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In this new era, where tremendous information is available on the internet, it is most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large documents of text. There are plenty of text material available on the internet. So, there is a problem of searching for relevant documents from the number of documents available and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.
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Parimoo, Rohit, Rohit Sharma, Naleen Gaur, Nimish Jain, and Sweeta Bansal. "Applying Text Rank to Build an Automatic Text Summarization Web Application." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 865–67. http://dx.doi.org/10.22214/ijraset.2022.40766.

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Abstract: Automatic Text Summarization is one of the most trending research areas in the field of Natural Language Processing. The main aim of text summarization is to reduce the size of a text without losing any important information. Various techniques can be used for automatic summarization of text. In this paper we are going to focus on the automatic summarization of text using graph-based methods. In particular, we are going to discuss the implementation of a general-purpose web application which performs automatic summarization on the text entered using the Text Rank Algorithm. Summarization of text using graph-based approaches involves pre-processing and cleansing of text, tokenizing the sentences present in the text, representing the tokenized text in the form of numerical vectors, creating a similarity matrix which shows the semantic similarity between different sentences present in the text, representing the similarity matrix as a graph, scoring and ranking the sentences and extracting the summary. Keywords: Text Summarization, Unsupervised Learning, Text Rank, Page Rank, Web Application, Graph Based Summarization, Extractive Summarization
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Jawale, Sakshi, Pranit Londhe, Prajwali Kadam, Sarika Jadhav, and Rushikesh Kolekar. "Automatic Text Summarization." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1842–46. http://dx.doi.org/10.22214/ijraset.2023.51815.

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Abstract: Text Summarization is a Natural Language Processing (NLP) method that extracts and collects data from the source and summarizes it. Text summarization has become a requirement for many applications since manually summarizing vast amounts of information is difficult, especially with the expanding magnitude of data. Financial research, search engine optimization, media monitoring, question-answering bots, and document analysis all benefit from text summarization. This paper extensively addresses several summarizing strategies depending on intent, volume of data, and outcome. Our aim is to evaluate and convey an abstract viewpoint of the present scenario research work for text summarization.
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Chettri, Roshna, and Udit Kr. "Automatic Text Summarization." International Journal of Computer Applications 161, no. 1 (March 15, 2017): 5–7. http://dx.doi.org/10.5120/ijca2017912326.

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Patil, Aarti, Komal Pharande, Dipali Nale, and Roshani Agrawal. "Automatic Text Summarization." International Journal of Computer Applications 109, no. 17 (January 16, 2015): 18–19. http://dx.doi.org/10.5120/19418-0910.

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Leiva, Luis A. "Responsive text summarization." Information Processing Letters 130 (February 2018): 52–57. http://dx.doi.org/10.1016/j.ipl.2017.10.007.

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Bichi, Abdulkadir Abubakar, Ruhaidah Samsudin, Rohayanti Hassan, Layla Rasheed Abdallah Hasan, and Abubakar Ado Rogo. "Graph-based extractive text summarization method for Hausa text." PLOS ONE 18, no. 5 (May 9, 2023): e0285376. http://dx.doi.org/10.1371/journal.pone.0285376.

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Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.
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Okumura, Manabu, Takahiro Fukusima, Hidetsugu Nanba, and Tsutomu Hirao. "Text Summarization Challenge 2 text summarization evaluation at NTCIR workshop 3." ACM SIGIR Forum 38, no. 1 (July 2004): 29–38. http://dx.doi.org/10.1145/986278.986284.

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Dissertations / Theses on the topic "Text summarization"

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Branavan, Satchuthananthavale Rasiah Kuhan. "High compression rate text summarization." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44368.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 95-97).
This thesis focuses on methods for condensing large documents into highly concise summaries, achieving compression rates on par with human writers. While the need for such summaries in the current age of information overload is increasing, the desired compression rate has thus far been beyond the reach of automatic summarization systems. The potency of our summarization methods is due to their in-depth modelling of document content in a probabilistic framework. We explore two types of document representation that capture orthogonal aspects of text content. The first represents the semantic properties mentioned in a document in a hierarchical Bayesian model. This method is used to summarize thousands of consumer reviews by identifying the product properties mentioned by multiple reviewers. The second representation captures discourse properties, modelling the connections between different segments of a document. This discriminatively trained model is employed to generate tables of contents for books and lecture transcripts. The summarization methods presented here have been incorporated into large-scale practical systems that help users effectively access information online.
by Satchuthananthavale Rasiah Kuhan Branavan.
S.M.
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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 phrases et de groupes de phrases) pour générer des résumés cross-lingues plus stables et informatifs. La plupart des applications du TALN, celle du résumé automatique y compris, utilisent une mesure de similarité pour analyser et comparer le sens des mots, des séquences de mots, des phrases et des textes. L’une des façons d'analyser cette similarité est de générer une représentation de ces phrases tenant compte de leur contenu. Le sens des phrases est défini par plusieurs éléments, tels que le contexte des mots et des expressions, l'ordre des mots et les informations précédentes. Des mesures simples, comme la mesure cosinus et la distance euclidienne, fournissent une mesure de similarité entre deux phrases. Néanmoins, elles n'analysent pas l'ordre des mots ou les séquences de mots. En analysant ces problèmes, nous proposons un modèle de réseau de neurones combinant des réseaux de neurones récurrents et convolutifs pour estimer la similarité sémantique d'une paire de phrases (ou de textes) en fonction des contextes locaux et généraux des mots. Sur le jeu de données analysé, notre modèle a prédit de meilleurs scores de similarité que les systèmes de base en analysant mieux le sens local et général des mots mais aussi des expressions multimots. Afin d'éliminer les redondances et les informations non pertinentes de phrases similaires, nous proposons de plus une nouvelle méthode de compression multiphrase, fusionnant des phrases au contenu similaire en compressions courtes. Pour ce faire, nous modélisons des groupes de phrases semblables par des graphes de mots. Ensuite, nous appliquons un modèle de programmation linéaire en nombres entiers qui guide la compression de ces groupes à partir d'une liste de mots-clés ; nous cherchons ainsi un chemin dans le graphe de mots qui a une bonne cohésion et qui contient le maximum de mots-clés. Notre approche surpasse les systèmes de base en générant des compressions plus informatives et plus correctes pour les langues française, portugaise et espagnole. Enfin, nous combinons les méthodes précédentes pour construire un système de résumé de texte cross-lingue. Notre système génère des résumés cross-lingue de texte en analysant l'information à la fois dans les langues source et cible, afin d’identifier les phrases les plus pertinentes. Inspirés par les méthodes de résumé de texte par compression en analyse monolingue, nous adaptons notre méthode de compression multiphrase pour ce problème afin de ne conserver que l'information principale. Notre système s'avère être performant pour compresser l'information redondante et pour préserver l'information pertinente, en améliorant les scores d'informativité sans perdre la qualité grammaticale des résumés cross-lingues du français vers l'anglais. En analysant les résumés cross-lingues depuis l’anglais, le français, le portugais ou l’espagnol, vers l’anglais ou le français, notre système améliore les systèmes par extraction de l'état de l'art pour toutes ces langues. En outre, une expérience complémentaire menée sur des transcriptions automatiques de vidéo montre que notre approche permet là encore d'obtenir des scores ROUGE meilleurs et plus stables, même pour ces documents qui présentent des erreurs grammaticales et des informations inexactes ou manquantes
The popularization of social networks and digital documents increased quickly the informationavailable on the Internet. However, this huge amount of data cannot be analyzedmanually. Natural Language Processing (NLP) analyzes the interactions betweencomputers and human languages in order to process and to analyze natural languagedata. NLP techniques incorporate a variety of methods, including linguistics, semanticsand statistics to extract entities, relationships and understand a document. Amongseveral NLP applications, we are interested, in this thesis, in the cross-language textsummarization which produces a summary in a language different from the languageof the source documents. We also analyzed other NLP tasks (word encoding representation,semantic similarity, sentence and multi-sentence compression) to generate morestable and informative cross-lingual summaries.Most of NLP applications (including all types of text summarization) use a kind ofsimilarity measure to analyze and to compare the meaning of words, chunks, sentencesand texts in their approaches. A way to analyze this similarity is to generate a representationfor these sentences that contains the meaning of them. The meaning of sentencesis defined by several elements, such as the context of words and expressions, the orderof words and the previous information. Simple metrics, such as cosine metric andEuclidean distance, provide a measure of similarity between two sentences; however,they do not analyze the order of words or multi-words. Analyzing these problems,we propose a neural network model that combines recurrent and convolutional neuralnetworks to estimate the semantic similarity of a pair of sentences (or texts) based onthe local and general contexts of words. Our model predicted better similarity scoresthan baselines by analyzing better the local and the general meanings of words andmulti-word expressions.In order to remove redundancies and non-relevant information of similar sentences,we propose a multi-sentence compression method that compresses similar sentencesby fusing them in correct and short compressions that contain the main information ofthese similar sentences. We model clusters of similar sentences as word graphs. Then,we apply an integer linear programming model that guides the compression of theseclusters based on a list of keywords. We look for a path in the word graph that has goodcohesion and contains the maximum of keywords. Our approach outperformed baselinesby generating more informative and correct compressions for French, Portugueseand Spanish languages. Finally, we combine these previous methods to build a cross-language text summarizationsystem. Our system is an {English, French, Portuguese, Spanish}-to-{English,French} cross-language text summarization framework that analyzes the informationin both languages to identify the most relevant sentences. Inspired by the compressivetext summarization methods in monolingual analysis, we adapt our multi-sentencecompression method for this problem to just keep the main information. Our systemproves to be a good alternative to compress redundant information and to preserve relevantinformation. Our system improves informativeness scores without losing grammaticalquality for French-to-English cross-lingual summaries. Analyzing {English,French, Portuguese, Spanish}-to-{English, French} cross-lingual summaries, our systemsignificantly outperforms extractive baselines in the state of the art for all these languages.In addition, we analyze the cross-language text summarization of transcriptdocuments. Our approach achieved better and more stable scores even for these documentsthat have grammatical errors and missing information
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Ozsoy, Makbule Gulcin. "Text Summarization Using Latent Semantic Analysis." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612988/index.pdf.

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Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.
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Mlynarski, Angela, and University of Lethbridge Faculty of Arts and Science. "Automatic text summarization in digital libraries." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/270.

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A digital library is a collection of services and information objects for storing, accessing, and retrieving digital objects. Automatic text summarization presents salient information in a condensed form suitable for user needs. This thesis amalgamates digital libraries and automatic text summarization by extending the Greenstone Digital Library software suite to include the University of Lethbridge Summarizer. The tool generates summaries, nouns, and non phrases for use as metadata for searching and browsing digital collections. Digital collections of newspapers, PDFs, and eBooks were created with summary metadata. PDF documents were processed the fastest at 1.8 MB/hr, followed by the newspapers at 1.3 MB/hr, with eBooks being the slowest at 0.9 MV/hr. Qualitative analysis on four genres: newspaper, M.Sc. thesis, novel, and poetry, revealed narrative newspapers were most suitable for automatically generated summarization. The other genres suffered from incoherence and information loss. Overall, summaries for digital collections are suitable when used with newspaper documents and unsuitable for other genres.
xiii, 142 leaves ; 28 cm.
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Singi, Reddy Dinesh Reddy. "Comparative text summarization of product reviews." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/7031.

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Master of Science
Department of Computing and Information Sciences
William H. Hsu
This thesis presents an approach towards summarizing product reviews using comparative sentences by sentiment analysis. Specifically, we consider the problem of extracting and scoring features from natural language text for qualitative reviews in a particular domain. When shopping for a product, customers do not find sufficient time to learn about all products on the market. Similarly, manufacturers do not have proper written sources from which to learn about customer opinions. The only available techniques involve gathering customer opinions, often in text form, from e-commerce and social networking web sites and analyzing them, which is a costly and time-consuming process. In this work I address these issues by applying sentiment analysis, an automated method of finding the opinion stated by an author about some entity in a text document. Here I first gather information about smart phones from many e-commerce web sites. I then present a method to differentiate comparative sentences from normal sentences, form feature sets for each domain, and assign a numerical score to each feature of a product and a weight coefficient obtained by statistical machine learning, to be used as a weight for that feature in ranking various products by linear combinations of their weighted feature scores. In this thesis I also explain what role comparative sentences play in summarizing the product. In order to find the polarity of each feature a statistical algorithm is defined using a small-to-medium sized data set. Then I present my experimental environment and results, and conclude with a review of claims and hypotheses stated at the outset. The approach specified in this thesis is evaluated using manual annotated trained data and also using data from domain experts. I also demonstrate empirically how different algorithms on this summarization can be derived from the technique provided by an annotator. Finally, I review diversified options for customers such as providing alternate products for each feature, top features of a product, and overall rankings for products.
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AGUIAR, C. Z. "Concept Maps Mining for Text Summarization." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/9846.

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Made available in DSpace on 2018-08-02T00:03:48Z (GMT). No. of bitstreams: 1 tese_11160_CamilaZacche_dissertacao_final.pdf: 5437260 bytes, checksum: 0c96c6b2cce9c15ea234627fad78ac9a (MD5) Previous issue date: 2017-03-31
8 Resumo Os mapas conceituais são ferramentas gráficas para a representação e construção do conhecimento. Conceitos e relações formam a base para o aprendizado e, portanto, os mapas conceituais têm sido amplamente utilizados em diferentes situações e para diferentes propósitos na educação, sendo uma delas a represent ação do texto escrito. Mes mo um gramá tico e complexo texto pode ser representado por um mapa conceitual contendo apenas conceitos e relações que represente m o que foi expresso de uma forma mais complicada. No entanto, a construção manual de um mapa conceit ual exige bastante tempo e esforço na identificação e estruturação do conhecimento, especialmente quando o mapa não deve representar os conceitos da estrutura cognitiva do autor. Em vez disso, o mapa deve representar os conceitos expressos em um texto. Ass im, várias abordagens tecnológicas foram propostas para facilitar o processo de construção de mapas conceituais a partir de textos. Portanto, esta dissertação propõe uma nova abordagem para a construção automática de mapas conceituais como sumarização de t extos científicos. A sumarização pretende produzir um mapa conceitual como uma representação resumida do texto, mantendo suas diversas e mais importantes características. A sumarização pode facilitar a compreensão dos textos, uma vez que os alunos estão te ntando lidar com a sobrecarga cognitiva causada pela crescente quantidade de informação textual disponível atualmente. Este crescimento também pode ser prejudicial à construção do conhecimento. Assim, consideramos a hipótese de que a sumarização de um text o representado por um mapa conceitual pode atribuir características importantes para assimilar o conhecimento do texto, bem como diminuir a sua complexidade e o tempo necessário para processá - lo. Neste contexto, realizamos uma revisão da literatura entre o s anos de 1994 e 2016 sobre as abordagens que visam a construção automática de mapas conceituais a partir de textos. A partir disso, construímos uma categorização para melhor identificar e analisar os recursos e as características dessas abordagens tecnoló gicas. Além disso, buscamos identificar as limitações e reunir as melhores características dos trabalhos relacionados para propor nossa abordagem. 9 Ademais, apresentamos um processo Concept Map Mining elaborado seguindo quatro dimensões : Descrição da Fonte de Dados, Definição do Domínio, Identificação de Elementos e Visualização do Mapa. Com o intuito de desenvolver uma arquitetura computacional para construir automaticamente mapas conceituais como sumarização de textos acadêmicos, esta pesquisa resultou na ferramenta pública CMBuilder , uma ferramenta online para a construção automática de mapas conceituais a partir de textos, bem como uma api java chamada ExtroutNLP , que contém bibliotecas para extração de informações e serviços públicos. Para alcançar o objetivo proposto, direcionados esforços para áreas de processamento de linguagem natural e recuperação de informação. Ressaltamos que a principal tarefa para alcançar nosso objetivo é extrair do texto as proposições do tipo ( conceito, rela ção, conceito ). Sob essa premissa, a pesquisa introduz um pipeline que compreende: regras gramaticais e busca em profundidade para a extração de conceitos e relações a partir do texto; mapeamento de preposição, resolução de anáforas e exploração de entidad es nomeadas para a rotulação de conceitos; ranking de conceitos baseado na análise de frequência de elementos e na topologia do mapa; e sumarização de proposição baseada na topologia do grafo. Além disso, a abordagem também propõe o uso de técnicas de apre ndizagem supervisionada de clusterização e classificação associadas ao uso de um tesauro para a definição do domínio do texto e construção de um vocabulário conceitual de domínios. Finalmente, uma análise objetiva para validar a exatidão da biblioteca Extr outNLP é executada e apresenta 0.65 precision sobre o corpus . Além disso, uma análise subjetiva para validar a qualidade do mapa conceitual construído pela ferramenta CMBuilder é realizada , apresentando 0.75/0.45 para precision / recall de conceitos e 0.57/ 0.23 para precision/ recall de relações em idioma inglês e apresenta ndo 0.68/ 0.38 para precision/ recall de conceitos e 0.41/ 0.19 para precision/ recall de relações em idioma português. Ademais , um experimento para verificar se o mapa conceitual sumarizado pe lo CMBuilder tem influência para a compreensão do assunto abordado em um texto é realizado , atingindo 60% de acertos para mapas extraídos de pequenos textos com questões de múltipla escolha e 77% de acertos para m apas extraídos de textos extensos com quest ões discursivas
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Casamayor, Gerard. "Semantically-oriented text planning for automatic summarization." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671530.

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Text summarization deals with the automatic creation of summaries from one or more documents, either by extracting fragments from the input text or by generating an abstract de novo. Research in recent years has become dominated by a new paradigm where summarization is addressed as a mapping from a sequence of tokens in an input document to a new sequence of tokens summarizing the input. Works following this paradigm apply supervised deep learning methods to learn sequence to sequence models from a large corpus of documents paired with human-crafted summaries. Despite impressive results in automatic quantitative evaluations, this approach to summarization also suffers from a number of drawbacks. One concern is that learned models tend to operate in a black-box fashion that prevents obtaining insights or results from intermediate analysis that could be applied to other tasks -an important consideration in many real-world scenarios where summaries are not the only desired output of a natural language processing system. Another significant drawback is that deep learning methods are largely constrained to languages and types of summary for which abundant corpora containing human authored summaries is available. Albeit researchers are experimenting with transfer learning methods to overcome this problem, it is far from clear how effective these methods are and how to apply them to scenarios where summaries need to adapt to a query or to user preferences. In those cases where it is not practical to learn a sequence to sequence model, it is convenient to fall back to a more traditional formulation of summarization where the input documents are first analyzed, then a summary is planned by selecting and organizing contents, and the final summary is generated either extractively or abstractively --using natural language generation methods in the latter case. By separating linguistic analysis, planning and generation, it becomes possible to apply different approaches to each task. This thesis focuses on the text planning step. Drawing from past research in word sense disambiguation, text summarization and natural language generation, this thesis presents an unsupervised approach to planning the production of summaries. Following the observation that a common strategy for both disambiguation and summarization tasks is to rank candidate items --meanings, text fragments-- we propose a strategy, at the core of our approach, that ranks candidate lexical meanings and individual words in a text. These ranks contribute towards the creation of a graph-based semantic representation from which we select non-redundant contents and organize them for inclusion in the summary. The overall approach is supported by lexicographic databases that provide cross-lingual and cross-domain knowledge, and by textual similarity methods used to compare meanings with each other and with the text. The methods presented in this thesis are tested on two separate tasks, disambiguation of word senses and named entities, and single-document extractive summarization of English texts. The evaluation of the disambiguation task shows that our approach produces useful results for tasks other than summarization, while evaluating in an extractive summarization setting allows us to compare our approach to existing summarization systems. While the results are inconclusive with respect to state-of-the-art in disambiguation and summarization systems, they hint at a large potential for our approach.
El resum automàtic de textos és una tasca dins del camp d'estudi de processament del llenguatge natural que versa sobre la creació automàtica de resums d'un o més documents, ja sigui extraient fragments del text d'entrada or generant un resum des de zero. La recerca recent en aquesta tasca ha estat dominada per un nou paradigma on el resum és abordat com un mapeig d'una seqüència de paraules en el document d'entrada a una nova seqüència de paraules que resumeixen el document. Els treballs que segueixen aquest paradigma apliquen mètodes d'aprenentatge supervisat profund per tal d'aprendre model seqüència a seqüència a partir d'un gran corpus de documents emparellats amb resums escrits a mà. Tot i els resultats impressionants en avaluacions quantitatives automàtiques, aquesta aproximació al resum automàtic també té alguns inconvenients. Un primer problema és que els models entrenats tendeixen a operar com una caixa negra que impedeix obtenir coneixements o resultats de representacions intermèdies i que puguin ser aplicat a altres tasques. Aquest és un problema important en situacions del món real on els resums no son l'única sortida que s'espera d'un sistema de processament de llenguatge natural. Un altre inconvenient significatiu és que els mètodes d'aprenentatge profund estan limitats a idiomes i tipus de resum pels que existeixen grans corpus amb resums escrits per humans. Tot i que els investigadors experimenten amb mètodes de transferència del coneixement per a superar aquest problema, encara ens trobem lluny de saber com d'efectius son aquests mètodes i com aplicar-los a situacions on els resums s'han d'adaptar a consultes o preferències formulades per l'usuari. En aquells casos en que no és pràctic aprendre models de seqüència a seqüència, convé tornar a una formulació més tradicional del resum automàtic on els documents d'entrada s'analitzen en primer lloc, es planifica el resum tot seleccionant i organitzant continguts i el resum final es genera per extracció o abstracció, fent servir mètodes de generació de llenguatge natural en aquest últim cas. Separar l'anàlisi lingüístic, la planificació i la generació permet aplicar estratègies diferents a cada tasca. Aquesta tesi tracta el pas central de planificació del resum. Inspirant-nos en recerca existent en desambiguació de sentits de mots, resum automàtic de textos i generació de llenguatge natural, aquesta tesi presenta una estratègia no supervisada per a la creació de resums. Seguim l'observació de que el rànquing d'ítems (significats o fragments de text) és un mètode comú per a tasques desambiguació i de resum, i proposem un mètode central per a la nostra estratègia que ordena significats lèxics i paraules d'un text. L'ordre resultant contribueix a la creació d'una representació semàntica en forma de graf des de la que seleccionem continguts no redundants i els organitzem per a la seva inclusió en el resum. L'estratègia general es fonamenta en bases de dades lexicogràfiques que proporcionen coneixement creuat entre múltiples idiomes i àrees temàtiques, i per mètodes de càlcul de similitud entre texts que fem servir per comparar significats entre sí i amb el text. Els mètodes que es presenten en aquesta tesi son posats a prova en dues tasques separades, la desambiguació de sentits de paraula i d'entitats amb nom, i el resum extractiu de documents en anglès. L'avaluació de la desambiguació mostra que la nostra estratègia produeix resultats útils per a tasques més enllà del resum automàtic, mentre que l'avaluació del resum extractiu ens permet comparar el nostre enfocament a sistemes existents de resum automàtic. Tot i que els nostres resultats no representen un avenç significatiu respecte a l'estat de la qüestió en desambiguació i resum automàtic, suggereixen que l'estratègia té un gran potencial.
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8

Reeve, Lawrence H. Han Hyoil. "Semantic annotation and summarization of biomedical text /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1779.

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Hassel, Martin. "Resource Lean and Portable Automatic Text Summarization." Doctoral thesis, Stockholm : Numerisk analys och datalogi Numerical Analysis and Computer Science, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4414.

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Lehto, Niko, and Mikael Sjödin. "Automatic text summarization of Swedish news articles." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159972.

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Abstract:
With an increasing amount of textual information available there is also an increased need to make this information more accessible. Our paper describes a modified TextRank model and investigates the different methods available to use automatic text summarization as a means for summary creation of swedish news articles. To evaluate our model we focused on intrinsic evaluation methods, in part through content evaluation in the form of of measuring referential clarity and non-redundancy, and in part by text quality evaluation measures, in the form of keyword retention and ROUGE evaluation. The results acquired indicate that stemming and improved stop word capabilities can have a positive effect on the ROUGE scores. The addition of redundancy checks also seems to have a positive effect on avoiding repetition of information. Keyword retention decreased somewhat, however. Lastly all methods had some trouble with dangling anaphora, showing a need for further work within anaphora resolution.
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Books on the topic "Text summarization"

1

Torres-Moreno, Juan-Manuel. Automatic Text Summarization. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.

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Inderjeet, Mani, and Maybury Mark T, eds. Advances in automatic text summarization. Cambridge, Mass: MIT Press, 1999.

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Kedzie, Christopher. Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation. [New York, N.Y.?]: [publisher not identified], 2021.

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H, Hovy Eduard, Radev Dragomir, and AAAI Symposium (1998 : Stanford, California), eds. Intelligent text summarization: Papers from the 1998 AAAI Symposium, March 23-25, Stanford, California. Menlo Park, Calif: AAAI Press, 1998.

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Marcu, Constantin Daniel. The rhetorical parsing, summarization, and generation of natural language texts. Toronto: University of Toronto, Dept. of Computer Science, 1998.

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Hovy, Eduard. Text Summarization. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0032.

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This article describes research and development on the automated creation of summaries of one or more texts. It defines the concept of summary and presents an overview of the principal approaches in summarization. It describes the design, implementation, and performance of various summarization systems. The stages of automated text summarization are topic identification, interpretation, and summary generation, each having its sub stages. Due to the challenges involved, multi-document summarization is much less developed than single-document summarization. This article reviews particular techniques used in several summarization systems. Finally, this article assesses the methods of evaluating summaries. This article reviews evaluation strategies, from previous evaluation studies, to the two-basic measures method. Summaries are so task and genre specific; therefore, no single measurement covers all cases of evaluation
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Torres-Moreno, Juan Manuel. Automatic Text Summarization. Wiley & Sons, Incorporated, John, 2014.

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Torres-Moreno, Juan-Manuel. Automatic Text Summarization. Wiley & Sons, Incorporated, John, 2014.

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Torres-Moreno, Juan-Manuel. Automatic Text Summarization. Wiley-Interscience, 2014.

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Torres-Moreno, Juan-Manuel. Automatic Text Summarization. Wiley & Sons, Incorporated, John, 2014.

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Book chapters on the topic "Text summarization"

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Shen, Dou. "Text Summarization." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_424-2.

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Shen, Dou. "Text Summarization." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_424-3.

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Sarbo, Janos J., Jozsef I. Farkas, and Auke J. J. van Breemen. "Text summarization." In Knowledge in Formation, 151–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17089-8_9.

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Jo, Taeho. "Text Summarization." In Studies in Big Data, 271–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91815-0_13.

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Sarkar, Dipanjan. "Text Summarization." In Text Analytics with Python, 217–63. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2388-8_5.

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Shen, Dou. "Text Summarization." In Encyclopedia of Database Systems, 3079–83. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_424.

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Sahin, Özgür. "Text Summarization." In Develop Intelligent iOS Apps with Swift, 121–35. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6421-8_6.

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Aggarwal, Charu C. "Text Summarization." In Machine Learning for Text, 361–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_11.

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Shen, Dou. "Text Summarization." In Encyclopedia of Database Systems, 4113–17. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_424.

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Aggarwal, Charu C. "Text Summarization." In Machine Learning for Text, 393–418. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96623-2_12.

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Conference papers on the topic "Text summarization"

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Kianmehr, Keivan, Shang Gao, Jawad Attari, M. Mushfiqur Rahman, Kofi Akomeah, Reda Alhajj, Jon Rokne, and Ken Barker. "Text summarization techniques." In the 11th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1806338.1806429.

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Kaddour, Abdelkader, Nassim Zellal, and Lamri Sayad. "Improving text classification using text summarization." In 2022 2nd International Conference on New Technologies of Information and Communication (NTIC). IEEE, 2022. http://dx.doi.org/10.1109/ntic55069.2022.10100492.

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Al-Taani, Ahmad T. "Automatic text summarization approaches." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8285983.

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Biswas, Sreyasi, Rasmita Rautray, Rasmita Dash, and Rajashree Dash. "Text Summarization: A Review." In 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2018. http://dx.doi.org/10.1109/icdsba.2018.00048.

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Binwahlan, Mohammed Salem, Naomie Salim, and Ladda Suanmali. "Swarm Based Text Summarization." In 2009 International Association of Computer Science and Information Technology - Spring Conference. IEEE, 2009. http://dx.doi.org/10.1109/iacsit-sc.2009.61.

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Okumura, Manabu, Takahiro Fukusima, and Hidetsugu Nanba. "Text summarization challenge 2." In the HLT-NAACL 03. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1119467.1119474.

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Seid, Ahmed Iman, Abdiqani Abdullahi Abdisalan, Mustafe Mohamed Abdulahi, Shantipriya Parida, and Satya Ranjan Dash. "Somali Extractive Text Summarization." In 2022 OITS International Conference on Information Technology (OCIT). IEEE, 2022. http://dx.doi.org/10.1109/ocit56763.2022.00063.

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Divya, S., and N. Sripriya. "Unsupervised hierarchical text summarization." In 2ND INTERNATIONAL CONFERENCE ON ENERGETICS, CIVIL AND AGRICULTURAL ENGINEERING 2021 (ICECAE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0116918.

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Khor, Yuen Kei, Chi Wee Tan, and Tong Ming Lim. "Text Summarization on Amazon Food Reviews using TextRank." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1011.

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Text summarization is a technique to create a summary by shortening the length of text but keep the key information. There are two main approaches to summarize the text which are abstractive summarization and extractive summarization. This study is aimed to extract the most important top 5 reviews which can summarize the overall reviews of certain product in Amazon fine food reviews. TextRank algorithm which is one of the extractive summarization approaches is used to perform text summarization automatically. GloVe pre-trained word embedding model with 100 dimensions is used to map each word from the reviews to vector representation. Besides, PageRank algorithm is applied to compute the sentence rankings scores to determine how important and relevant of the sentences can be the representatives of summary. Top 5 reviews with the highest sentence ranking scores are extracted to be the summary and further discussed the customer perception on the product based on the summary generated. The final summary shows that Amazon customer reviews tend to positive for certain food brand. Keywords: Text Summarization, Extractive Summarization, TextRank
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Agrawal, Kanika. "Legal Case Summarization: An Application for Text Summarization." In 2020 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2020. http://dx.doi.org/10.1109/iccci48352.2020.9104093.

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Reports on the topic "Text summarization"

1

Firmin, Therese, and Inderjeet Mani. Automatic Text Summarization in Tipster. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada632154.

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President, Stacy F., and Bonnie J. Dorr. Text Summarization Evaluation: Correlating Human Performance on an Extrinsic Task with Automatic Intrinsic Metrics. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada455670.

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