Relevant bibliographies by topics / Summarization

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Academic literature on the topic 'Summarization'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Summarization"

1

da Cunha, Iria, Leo Wanner, and Teresa Cabré. "Summarization of specialized discourse." Terminology 13, no. 2 (November 19, 2007): 249–86. http://dx.doi.org/10.1075/term.13.2.07cun.

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In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The model takes into account the results of an empirical study which reveals that, on the one hand, domain-specific summarization criteria can often be derived from the summaries of domain specialists, and, on the other hand, adequate summarization strategies must be multidimensional, i.e., cover various types of linguistic clues. We take into account the textual, lexical, discursive, syntactic and communicative dimensions. This is novel in the field of summarization. The experiments carried out so far indicate that our model is suitable to provide high quality summarizations.
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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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Blekanov, Ivan S., Nikita Tarasov, and Svetlana S. Bodrunova. "Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages." Future Internet 14, no. 3 (February 23, 2022): 69. http://dx.doi.org/10.3390/fi14030069.

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Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.
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Pei, Jisheng, and Xiaojun Ye. "Information-Balance-Aware Approximated Summarization of Data Provenance." Scientific Programming 2017 (September 12, 2017): 1–11. http://dx.doi.org/10.1155/2017/4504589.

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Extracting useful knowledge from data provenance information has been challenging because provenance information is often overwhelmingly enormous for users to understand. Recently, it has been proposed that we may summarize data provenance items by grouping semantically related provenance annotations so as to achieve concise provenance representation. Users may provide their intended use of the provenance data in terms of provisioning, and the quality of provenance summarization could be optimized for smaller size and closer distance between the provisioning results derived from the summarization and those from the original provenance. However, apart from the intended provisioning use, we notice that more dedicated and diverse user requirements can be expressed and considered in the summarization process by assigning importance weights to provenance elements. Moreover, we introduce information balance index (IBI), an entropy based measurement, to dynamically evaluate the amount of information retained by the summary to check how it suits user requirements. An alternative provenance summarization algorithm that supports manipulation of information balance is presented. Case studies and experiments show that, in summarization process, information balance can be effectively steered towards user-defined goals and requirement-driven variants of the provenance summarizations can be achieved to support a series of interesting scenarios.
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Bhatia, Neelima, and Arunima Jaiswal. "Literature Review on Automatic Text Summarization: Single and Multiple Summarizations." International Journal of Computer Applications 117, no. 6 (May 20, 2015): 25–29. http://dx.doi.org/10.5120/20560-2948.

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Zhang, Qianjin, Dahai Jin, Yawen Wang, and Yunzhan Gong. "Statement-Grained Hierarchy Enhanced Code Summarization." Electronics 13, no. 4 (February 15, 2024): 765. http://dx.doi.org/10.3390/electronics13040765.

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Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.
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S, Sai Shashank, Sindhu S, Vineeth V, and Pranathi C. "VIDEO SUMMARIZATION." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 277–80. http://dx.doi.org/10.26562/irjcs.2022.v0908.24.

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The general public now has access to a vast amount of multimedia information thanks to recent technological advancements and the quick expansion of consumer electronics, making it challenging to effectively consume video material among the thousands of options accessible. By choosing and presenting the most educational or fascinating materials for users, we provide a method to quickly summarize the content of a lengthy video document. The practice of condensing a raw video into a more manageable form without losing much information is known as video summarizing. Either a comprehensive analysis of the full movie or the local differences between neighboring frames are used to achieve this. The majority of such approaches rely on universal characteristics like color, texture, motion data, etc. Video summaries are evaluated depending on the sort of content they are formed from (object, event, perception, or feature-based) and the functionality made available to the user for consumption (interactive or static, personalized or generic). The suggested system analyses each frame of a video as input before producing a summary. Each frame receives a score that is used to compare it to a threshold value in the final phase. Every frame whose frame score exceeds the threshold is chosen as a key frame and is represented in the final movie summary. This technique enables us to condense video information of various lengths while guaranteeing that the key moments are included. The purpose of video summary is to facilitate quick access, speed up browsing through a sizable video database, and offer a condensed video representation while maintaining the core activities of the original video.
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Nenkova, Ani. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 2 (2011): 103–233. http://dx.doi.org/10.1561/1500000015.

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Larson, Martha. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 3 (2012): 235–422. http://dx.doi.org/10.1561/1500000020.

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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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Dissertations / Theses on the topic "Summarization"

1

Bosma, Wauter Eduard. "Discourse oriented summarization." Enschede : Centre for Telematics and Information Technology (CTIT), 2008. http://doc.utwente.nl/58836.

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Moon, Brandon B. "Interactive football summarization /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3337.pdf.

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Moon, Brandon B. "Interactive Football Summarization." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1999.

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Football fans do not have the time to watch every game in its entirety and need an effective solution that summarizes them the story of the game. Human-generated summaries are often too short, requiring time and resources to create. We utilize the advantages of Interactive TV to create an automatic football summarization service that is cohesive, provides context, covers the necessary plays, and is concise. First, we construct a degree of interest function that ranks each play based on detailed, play-by-play game events as well as viewing statistics collected from an interactive viewing environment. This allows us to select the plays that are important to the game as well as those that are interesting to the viewer. Second, we create a visual transition that shows the progress of the ball whenever plays are skipped, allowing the viewer to understand the context of each play within the summary. Third, we enable interactive controls that allow viewers to manipulate the summary and delve deeper into the actual game whenever they wish. We validate our solution through two user studies—one to ensure that our degree of interest function selects the plays that are most interesting to the viewer, and the other to show that our transitions and interactive controls provide a better understanding of the game. We conclude that our summary solution is effective at conveying the story of a football game.
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Sizov, Gleb. "Extraction-Based Automatic Summarization : Theoretical and Empirical Investigation of Summarization Techniques." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10861.

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A summary is a shortened version of a text that contains the main points of the original content. Automatic summarization is the task of generating a summary by a computer. For example, given a collection of news articles for the last week an automatic summarizer is able to create a concise overview of the important events. This summary can be used as the replacement for the original content or help to identify the events that a person is particularly interested in. Potentially, automatic summarization can save a lot of time for people that deal with a large amount of textual information. The straightforward way to generate a summary is to select several sentences from the original text and organize them in way to create a coherent text. This approach is called extraction-based summarization and is the topic of this thesis. Extraction-based summarization is a complex task that consists of several challenging subtasks. The essential part of the extraction-based approach is identification of sentences that contain important information. It can be done using graph-based representations and centrality measures that exploit similarities between sentences to identify the most central sentences. This thesis provide a comprehensive overview of methods used in extraction-based automatic summarization. In addition, several general natural language processing issues such as feature selection and text representation models are discussed with regard to automatic summarization. Part of the thesis is dedicated to graph-based representations and centrality measures used in extraction-based summarization. Theoretical analysis is reinforced with the experiments using the summarization framework implemented for this thesis. The task for the experiments is query-focused multi-document extraction-based summarization, that is, summarization of several documents according to a user query. The experiments investigate several approaches to this task as well as the use of different representation models, similarity and centrality measures. The obtained results indicate that use of graph centrality measures significantly improves the quality of generated summaries. Among the variety of centrality measure the degree-based ones perform better than path-based measures. The best performance is achieved when centralities are combined with redundancy removal techniques that prevent inclusion of similar sentences in a summary. Experiments with representation models reveal that a simple local term count representation performs better than the distributed representation based on latent semantic analysis, which indicates that further investigation of distributed representations in regard to automatic summarization is necessary. The implemented system performs quite good compared with the systems that participated in DUC 2007 summarization competition. Nevertheless, manual inspection of the generated summaries demonstrate some of the flaws of the implemented summarization mechanism that can be addressed by introducing advanced algorithms for sentence simplification and sentence ordering.

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Chellal, Abdelhamid. "Event summarization on social media stream : retrospective and prospective tweet summarization." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30118/document.

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Le contenu généré dans les médias sociaux comme Twitter permet aux utilisateurs d'avoir un aperçu rétrospectif d'évènement et de suivre les nouveaux développements dès qu'ils se produisent. Cependant, bien que Twitter soit une source d'information importante, il est caractérisé par le volume et la vélocité des informations publiées qui rendent difficile le suivi de l'évolution des évènements. Pour permettre de mieux tirer profit de ce nouveau vecteur d'information, deux tâches complémentaires de recherche d'information dans les médias sociaux ont été introduites : la génération de résumé rétrospectif qui vise à sélectionner les tweets pertinents et non redondant récapitulant "ce qui s'est passé" et l'envoi des notifications prospectives dès qu'une nouvelle information pertinente est détectée. Notre travail s'inscrit dans ce cadre. L'objectif de cette thèse est de faciliter le suivi d'événement, en fournissant des outils de génération de synthèse adaptés à ce vecteur d'information. Les défis majeurs sous-jacents à notre problématique découlent d'une part du volume, de la vélocité et de la variété des contenus publiés et, d'autre part, de la qualité des tweets qui peut varier d'une manière considérable. La tâche principale dans la notification prospective est l'identification en temps réel des tweets pertinents et non redondants. Le système peut choisir de retourner les nouveaux tweets dès leurs détections où bien de différer leur envoi afin de s'assurer de leur qualité. Dans ce contexte, nos contributions se situent à ces différents niveaux : Premièrement, nous introduisons Word Similarity Extended Boolean Model (WSEBM), un modèle d'estimation de la pertinence qui exploite la similarité entre les termes basée sur le word embedding et qui n'utilise pas les statistiques de flux. L'intuition sous- jacente à notre proposition est que la mesure de similarité à base de word embedding est capable de considérer des mots différents ayant la même sémantique ce qui permet de compenser le non-appariement des termes lors du calcul de la pertinence. Deuxièmement, l'estimation de nouveauté d'un tweet entrant est basée sur la comparaison de ses termes avec les termes des tweets déjà envoyés au lieu d'utiliser la comparaison tweet à tweet. Cette méthode offre un meilleur passage à l'échelle et permet de réduire le temps d'exécution. Troisièmement, pour contourner le problème du seuillage de pertinence, nous utilisons un classificateur binaire qui prédit la pertinence. L'approche proposée est basée sur l'apprentissage supervisé adaptatif dans laquelle les signes sociaux sont combinés avec les autres facteurs de pertinence dépendants de la requête. De plus, le retour des jugements de pertinence est exploité pour re-entrainer le modèle de classification. Enfin, nous montrons que l'approche proposée, qui envoie les notifications en temps réel, permet d'obtenir des performances prometteuses en termes de qualité (pertinence et nouveauté) avec une faible latence alors que les approches de l'état de l'art tendent à favoriser la qualité au détriment de la latence. Cette thèse explore également une nouvelle approche de génération du résumé rétrospectif qui suit un paradigme différent de la majorité des méthodes de l'état de l'art. Nous proposons de modéliser le processus de génération de synthèse sous forme d'un problème d'optimisation linéaire qui prend en compte la diversité temporelle des tweets. Les tweets sont filtrés et regroupés d'une manière incrémentale en deux partitions basées respectivement sur la similarité du contenu et le temps de publication. Nous formulons la génération du résumé comme étant un problème linéaire entier dans lequel les variables inconnues sont binaires, la fonction objective est à maximiser et les contraintes assurent qu'au maximum un tweet par cluster est sélectionné dans la limite de la longueur du résumé fixée préalablement
User-generated content on social media, such as Twitter, provides in many cases, the latest news before traditional media, which allows having a retrospective summary of events and being updated in a timely fashion whenever a new development occurs. However, social media, while being a valuable source of information, can be also overwhelming given the volume and the velocity of published information. To shield users from being overwhelmed by irrelevant and redundant posts, retrospective summarization and prospective notification (real-time summarization) were introduced as two complementary tasks of information seeking on document streams. The former aims to select a list of relevant and non-redundant tweets that capture "what happened". In the latter, systems monitor the live posts stream and push relevant and novel notifications as soon as possible. Our work falls within these frameworks and focuses on developing a tweet summarization approaches for the two aforementioned scenarios. It aims at providing summaries that capture the key aspects of the event of interest to help users to efficiently acquire information and follow the development of long ongoing events from social media. Nevertheless, tweet summarization task faces many challenges that stem from, on one hand, the high volume, the velocity and the variety of the published information and, on the other hand, the quality of tweets, which can vary significantly. In the prospective notification, the core task is the relevancy and the novelty detection in real-time. For timeliness, a system may choose to push new updates in real-time or may choose to trade timeliness for higher notification quality. Our contributions address these levels: First, we introduce Word Similarity Extended Boolean Model (WSEBM), a relevance model that does not rely on stream statistics and takes advantage of word embedding model. We used word similarity instead of the traditional weighting techniques. By doing this, we overcome the shortness and word mismatch issues in tweets. The intuition behind our proposition is that context-aware similarity measure in word2vec is able to consider different words with the same semantic meaning and hence allows offsetting the word mismatch issue when calculating the similarity between a tweet and a topic. Second, we propose to compute the novelty score of the incoming tweet regarding all words of tweets already pushed to the user instead of using the pairwise comparison. The proposed novelty detection method scales better and reduces the execution time, which fits real-time tweet filtering. Third, we propose an adaptive Learning to Filter approach that leverages social signals as well as query-dependent features. To overcome the issue of relevance threshold setting, we use a binary classifier that predicts the relevance of the incoming tweet. In addition, we show the gain that can be achieved by taking advantage of ongoing relevance feedback. Finally, we adopt a real-time push strategy and we show that the proposed approach achieves a promising performance in terms of quality (relevance and novelty) with low cost of latency whereas the state-of-the-art approaches tend to trade latency for higher quality. This thesis also explores a novel approach to generate a retrospective summary that follows a different paradigm than the majority of state-of-the-art methods. We consider the summary generation as an optimization problem that takes into account the topical and the temporal diversity. Tweets are filtered and are incrementally clustered in two cluster types, namely topical clusters based on content similarity and temporal clusters that depends on publication time. Summary generation is formulated as integer linear problem in which unknowns variables are binaries, the objective function is to be maximized and constraints ensure that at most one post per cluster is selected with respect to the defined summary length limit
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Nahnsen, Thade. "Automation of summarization evaluation methods and their application to the summarization process." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5278.

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Summarization is the process of creating a more compact textual representation of a document or a collection of documents. In view of the vast increase in electronically available information sources in the last decade, filters such as automatically generated summaries are becoming ever more important to facilitate the efficient acquisition and use of required information. Different methods using natural language processing (NLP) techniques are being used to this end. One of the shallowest approaches is the clustering of available documents and the representation of the resulting clusters by one of the documents; an example of this approach is the Google News website. It is also possible to augment the clustering of documents with a summarization process, which would result in a more balanced representation of the information in the cluster, NewsBlaster being an example. However, while some systems are already available on the web, summarization is still considered a difficult problem in the NLP community. One of the major problems hampering the development of proficient summarization systems is the evaluation of the (true) quality of system-generated summaries. This is exemplified by the fact that the current state-of-the-art evaluation method to assess the information content of summaries, the Pyramid evaluation scheme, is a manual procedure. In this light, this thesis has three main objectives. 1. The development of a fully automated evaluation method. The proposed scheme is rooted in the ideas underlying the Pyramid evaluation scheme and makes use of deep syntactic information and lexical semantics. Its performance improves notably on previous automated evaluation methods. 2. The development of an automatic summarization system which draws on the conceptual idea of the Pyramid evaluation scheme and the techniques developed for the proposed evaluation system. The approach features the algorithm for determining the pyramid and bases importance on the number of occurrences of the variable-sized contributors of the pyramid as opposed to word-based methods exploited elsewhere. 3. The development of a text coherence component that can be used for obtaining the best ordering of the sentences in a summary.
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Smith, Christian. "Automatic summarization and readability." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68332.

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The enormous amount of information available today within different media gives rise to the notion of ways to reduce the inevitable complexity and to distribute text material to different channels or media. In an effort to investigate the possibilities of a tool to help eleviate the problem, an automatic summarizer called COGSUM has been developed and evaluated with regards to the informational quality of the summaries and with regards to the readability. COGSUM is based on word space methodology, including virtues such as problematic computational complexity and possibilities of inferring semantic relations. The results from the evaluations show how to set some parameters in order to get as good summary as possible and that the resulting summaries have higher readability score than the full text on different genres.
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Seidlhofer, Barbara. "Discourse analysis for summarization." Thesis, University College London (University of London), 1991. http://discovery.ucl.ac.uk/10018780/.

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Summarization is an activity which language students are frequently called upon to perform, often without any explicit guidance. In a wider sense, it might be said that all learning, whether of language or anything else, involves the ability to distinguish what is important from what is not, and to incorporate it into existing schematic knowledge. In this respect, summarization can be seen as central to education in general as well as language education in particular. This thesis is an attempt to gain insights into the essential criteria for summarization. After the first chapter has outlined the scope and methodology of the enquiry, chapters 2 to 5 review a number of models of text analysis and discourse processing which, on the face of it, promise to provide a systematic basis for the identification of "main ideas" in written texts. It reviews a number of models of text analysis and discourse processing which, on the face of it, promise to provide a systematic basis for the identification of "main ideas" in written texts. These include the analysis of thematic structure associated with the work of Halliday and the Prague School, the Macrostructures proposed by van Dijk and Kintsch, and Meyer's studies of rhetorical structure. A critical investigation of these models leads to a consideration of a very different approach which focuses not on the text itself as product but on the reader's reaction to it in the process of interpretation. This emerges from the empirical analysis of student summaries and accounts in chapter 6, and is further discussed in the last chapter. In general, the thesis considers the theoretical validity of these different approaches to text description and their practical utility as points of reference for summarization. It surveys applied work based on them, relates them empirically to the analysis of summaries and accounts elicited from advanced Austrian students of English at university level, and works its way towards a set of principles and procedures which might be made operational in language pedagogy.
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Ceylan, Hakan. "Investigating the Extractive Summarization of Literary Novels." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103298/.

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Abstract Due to the vast amount of information we are faced with, summarization has become a critical necessity of everyday human life. Given that a large fraction of the electronic documents available online and elsewhere consist of short texts such as Web pages, news articles, scientific reports, and others, the focus of natural language processing techniques to date has been on the automation of methods targeting short documents. We are witnessing however a change: an increasingly larger number of books become available in electronic format. This means that the need for language processing techniques able to handle very large documents such as books is becoming increasingly important. This thesis addresses the problem of summarization of novels, which are long and complex literary narratives. While there is a significant body of research that has been carried out on the task of automatic text summarization, most of this work has been concerned with the summarization of short documents, with a particular focus on news stories. However, novels are different in both length and genre, and consequently different summarization techniques are required. This thesis attempts to close this gap by analyzing a new domain for summarization, and by building unsupervised and supervised systems that effectively take into account the properties of long documents, and outperform the traditional extractive summarization systems typically addressing news genre.
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Demirtas, Kezban. "Automatic Video Categorization And Summarization." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611113/index.pdf.

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In this thesis, we make automatic video categorization and summarization by using subtitles of videos. We propose two methods for video categorization. The first method makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The method starts with text preprocessing. Then a keyword extraction algorithm and a word sense disambiguation method are applied. The WordNet domains that correspond to the correct senses of keywords are extracted. Video is assigned a category label based on the extracted domains. The second method has the same steps for extracting WordNet domains of video but makes categorization by using a learning module. Experiments with documentary videos give promising results in discovering the correct categories of videos. Video summarization algorithms present condensed versions of a full length video by identifying the most significant parts of the video. We propose a video summarization method using the subtitles of videos and text summarization techniques. We identify significant sentences in the subtitles of a video by using text summarization techniques and then we compose a video summary by finding the video parts corresponding to these summary sentences.
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Books on the topic "Summarization"

1

Automatic summarization. Amsterdam: J. Benjamins Pub. Co., 2001.

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2

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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3

Wormeli, Rick. Summarization in Any Subject. Alexandria: ASCD, 2009.

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4

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

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Mehta, Parth, and Prasenjit Majumder. From Extractive to Abstractive Summarization: A Journey. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8934-4.

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Poibeau, Thierry, Horacio Saggion, Jakub Piskorski, and Roman Yangarber, eds. Multi-source, Multilingual Information Extraction and Summarization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28569-1.

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Mirkin, Boris. Core Data Analysis: Summarization, Correlation, and Visualization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00271-8.

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Seidlhofer, Barbara. Approaches to summarization: Discourse analysis and language education. Tübingen: G. Narr, 1995.

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Ouyang, Jessica Jin. Adapting Automatic Summarization to New Sources of Information. [New York, N.Y.?]: [publisher not identified], 2019.

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Mirkin, Boris. Core Concepts in Data Analysis: Summarization, Correlation and Visualization. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-287-2.

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Book chapters on the topic "Summarization"

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems, 1–8. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_953-2.

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems, 2884–89. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_953.

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Simske, Steven, and Marie Vans. "Summarization." In Functional Applications of Text Analytics Systems, 35–86. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338222-2.

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Lu, Wen-Jun, and Lei Zhu. "Summarization." In Multi-Mode Resonant Antennas, 251–56. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003291633-7.

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems, 3847–54. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_953.

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Torres-Moreno, Juan-Manuel. "Single-Document Summarization." In Automatic Text Summarization, 53–108. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch3.

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Bhatia, Surbhi, Poonam Chaudhary, and Nilanjan Dey. "Opinion Summarization." In Opinion Mining in Information Retrieval, 81–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5043-0_6.

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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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Belguith, Lamia Hadrich, Mariem Ellouze, Mohamed Hedi Maaloul, Maher Jaoua, Fatma Kallel Jaoua, and Philippe Blache. "Automatic Summarization." In Natural Language Processing of Semitic Languages, 371–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_12.

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Conference papers on the topic "Summarization"

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Qiu, Yunjian, and Yan Jin. "Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70866.

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Abstract In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the automatic evaluation method like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the statistical evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. It can be concluded that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific datasets.
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Goldstein, Jade, and Jaime Carbonell. "Summarization." In a workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/1119089.1119120.

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Zhang, Jin, Xueqi Cheng, and Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wi-iatw.2007.84.

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Zhang, Jin, Xueqi Cheng, and Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wiiatw.2007.4427541.

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Chen, Xiuying, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, and Rui Yan. "Learning towards Abstractive Timeline Summarization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/686.

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Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model (MTS).Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process.Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events.Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
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Christensen, Janara, Stephen Soderland, Gagan Bansal, and Mausam. "Hierarchical Summarization: Scaling Up Multi-Document Summarization." In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-1085.

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Kalnikaité, Vaiva, and Steve Whittaker. "Social summarization." In the ACM 2008 conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1460563.1460567.

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Chakraborty, Sunandan, Zohaib Jabbar, and Lakshminarayanan Subramanian. "Summarization Search." In ACM DEV '15: Annual Symposium on Computing for Development. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2830629.2835217.

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Lerman, Kevin, and Ryan McDonald. "Contrastive summarization." In Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1620853.1620886.

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Lerman, Kevin, Sasha Blair-Goldensohn, and Ryan McDonald. "Sentiment summarization." In the 12th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609124.

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Reports on the topic "Summarization"

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Tabassi, Elham, and Patrick Grother. Quality summarization :. Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7422.

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White, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff. Multidocument Summarization via Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada457772.

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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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DeMenthon, Daniel, Vikrant Kobla, and David Doermann. Video Summarization by Curve Simplification. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada459300.

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De Bock, Jelle, and Steven Verstockt. Automatic Summarization of Cyclocross Races. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317529.

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Sekine, Satoshi, and Chikashi Nobata. A Survey for Multi-Document Summarization. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460234.

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Daume III, Hal, and Daniel Marcu. Generic Sentence Fusion is an Ill-Defined Summarization Task. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada461416.

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Siddharthan, Advaith, Ani Nenkova, and Kathleen McKeown. Syntactic Simplification for Improving Content Selection in Multi-Document Summarization. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada457833.

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Kaplin, David B. Automatic Summarization with Sloth (Summarizes Lengthy Documents and Outputs The Highlights). Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408523.

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Dorr, Bonnie, and Terry Gaasterland. Summarization-Inspired Temporal-Relation Extraction: Tense-Pair Templates and Treebank-3 Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada460392.

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