Готові списки джерел за темами / Personalized summarization

Добірка наукової літератури з теми "Personalized summarization"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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Статті в журналах з теми "Personalized summarization"

Díaz, Alberto, and Pablo Gervás. "User-model based personalized summarization." Information Processing & Management 43, no. 6 (November 2007): 1715–34. http://dx.doi.org/10.1016/j.ipm.2007.01.009.

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Chen, Fan, Christophe De Vleeschouwer, and Andrea Cavallaro. "Resource Allocation for Personalized Video Summarization." IEEE Transactions on Multimedia 16, no. 2 (February 2014): 455–69. http://dx.doi.org/10.1109/tmm.2013.2291967.

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Li, Junjie, Haoran Li, and Chengqing Zong. "Towards Personalized Review Summarization via User-Aware Sequence Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6690–97. http://dx.doi.org/10.1609/aaai.v33i01.33016690.

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We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.

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WU, XINDONG, FEI XIE, GONGQING WU, and WEI DING. "PNFS: PERSONALIZED WEB NEWS FILTERING AND SUMMARIZATION." International Journal on Artificial Intelligence Tools 22, no. 05 (October 2013): 1360007. http://dx.doi.org/10.1142/s0218213013600075.

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Information on the World Wide Web is congested with large amounts of news contents. Recommending, filtering, and summarization of Web news have become hot topics of research in Web intelligence, aiming to find interesting news for users and give concise content for reading. This paper presents our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. Two Web news recommendation methods are proposed to keep tracking news and find topic interesting news for users. A keyword knowledge base is maintained and provides real-time updates to reflect the news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. A keyword extraction method based on lexical chains is proposed that uses the semantic similarity and the relatedness degree to represent the semantic relations between words. Word sense disambiguation is also performed in the built lexical chains. Experiments on Web news pages and journal articles show that the proposed keyword extraction method is effective. An example run of our PNFS system demonstrates the superiority of this Web intelligence system.

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Ul Haq, Ijaz, Amin Ullah, Khan Muhammad, Mi Young Lee, and Sung Wook Baik. "Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition." Complexity 2019 (May 5, 2019): 1–10. http://dx.doi.org/10.1155/2019/3581419.

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Анотація:

Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods.

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Zhang, Yujia, Michael Kampffmeyer, Xiaoguang Zhao, and Min Tan. "Deep Reinforcement Learning for Query-Conditioned Video Summarization." Applied Sciences 9, no. 4 (February 21, 2019): 750. http://dx.doi.org/10.3390/app9040750.

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Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given query. Thus it can be tailored to different user interests leading to a better personalized summary and differs from the generic video summarization which only focuses on video content. Our work targets this query-conditioned video summarization task, by first proposing a Mapping Network (MapNet) in order to express how related a shot is to a given query. MapNet helps establish the relation between the two different modalities (videos and query), which allows mapping of visual information to query space. After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. These rewards jointly guide the agent to select the most representative and diversity video shots that are most related to the user query. Experimental results on a query-conditioned video summarization benchmark demonstrate the effectiveness of our proposed method, indicating the usefulness of the proposed mapping mechanism as well as the reinforcement learning approach.

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CHEN, Sinan, and Masahide NAKAMURA. "Generating Personalized Dialogues Based on Conversation Log Summarization." Proceedings of Design & Systems Conference 2021.31 (2021): 3407. http://dx.doi.org/10.1299/jsmedsd.2021.31.3407.

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Li, Jianxin, Chengfei Liu, Jeffrey Xu Yu, Yi Chen, Timos Sellis, and J. Shane Culpepper. "Personalized Influential Topic Search via Social Network Summarization." IEEE Transactions on Knowledge and Data Engineering 28, no. 7 (July 1, 2016): 1820–34. http://dx.doi.org/10.1109/tkde.2016.2542804.

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Liang, Chao, Changsheng Xu, and Hanqing Lu. "Personalized Sports Video Customization Using Content and Context Analysis." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/836357.

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We present an integrated framework on personalized sports video customization, which addresses three research issues: semantic video annotation, personalized video retrieval and summarization, and system adaptation. Sports video annotation serves as the foundation of the video customization system. To acquire detailed description of video content, external web text is adopted to align with the related sports video according to their semantic correspondence. Based on the derived semantic annotation, a user-participant multiconstraint 0/1 Knapsack model is designed to model the personalized video customization, which can unify both video retrieval and summarization with different fusion parameters. As a measure to make the system adaptive to the particular user, a social network based system adaptation algorithm is proposed to learn latent user preference implicitly. Both quantitative and qualitative experiments conducted on twelve broadcast basketball and football videos validate the effectiveness of the proposed method.

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Chen, Yu-Hsiu, Pin-Yu Chen, Hong-Han Shuai, and Wen-Chih Peng. "TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7538–45. http://dx.doi.org/10.1609/aaai.v34i05.6252.

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We address personalized Electronic Direct Mail (EDM) subject generation, which generates an attractive subject line for a product description according to user's preference on different contents or writing styles. Generating personalized EDM subjects has a few notable differences from generating text summaries. The subject has to be not only faithful to the description itself but also attractive to increase the click-through rate. Moreover, different users may have different preferences over the styles of topics. We propose a novel personalized EDM subject generation model named Soft Template-based Personalized EDM Subject Generator (TemPEST) to consider the aforementioned users' characteristics when generating subjects, which contains a soft template-based selective encoder network, a user rating encoder network, a summary decoder network and a rating decoder. Experimental results indicate that TemPEST is able to generate personalized topics and also effectively perform recommending rating reconstruction.

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Більше джерел

Дисертації з теми "Personalized summarization"

El, Aouad Sara. "Personalized, Aspect-based Summarization of Movie Reviews." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS019.pdf.

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Анотація:

Les sites web de critiques en ligne aident les utilisateurs à décider quoi acheter ou quels hôtels choisir. Ces plateformes permettent aux utilisateurs d’exprimer leurs opinions à l’aide d’évaluations numériques et de commentaires textuels. Les notes numériques donnent une idée approximative du service. D'autre part, les commentaires textuels donnent des détails complets, ce qui est fastidieux à lire. Dans cette thèse, nous développons de nouvelles méthodes et algorithmes pour générer des résumés personnalisés de critiques de films, basés sur les aspects, pour un utilisateur donné. Le premier problème que nous abordons consiste à extraire un ensemble de mots liés à un aspect des critiques de films. Notre évaluation montre que notre méthode est capable d'extraire même des termes impopulaires qui représentent un aspect, tels que des termes composés ou des abréviations. Nous étudions ensuite le problème de l'annotation des phrases avec des aspects et proposons une nouvelle méthode qui annote les phrases en se basant sur une similitude entre la signature d'aspect et les termes de la phrase. Le troisième problème que nous abordons est la génération de résumés personnalisés, basés sur les aspects. Nous proposons un algorithme d'optimisation pour maximiser la couverture des aspects qui intéressent l'utilisateur et la représentativité des phrases dans le résumé sous réserve de contraintes de longueur et de similarité. Enfin, nous réalisons trois études d’utilisateur qui montrent que l’approche que nous proposons est plus performante que la méthode de pointe en matière de génération de résumés
Online reviewing websites help users decide what to buy or places to go. These platforms allow users to express their opinions using numerical ratings as well as textual comments. The numerical ratings give a coarse idea of the service. On the other hand, textual comments give full details which is tedious for users to read. In this dissertation, we develop novel methods and algorithms to generate personalized, aspect-based summaries of movie reviews for a given user. The first problem we tackle is extracting a set of related words to an aspect from movie reviews. Our evaluation shows that our method is able to extract even unpopular terms that represent an aspect, such as compound terms or abbreviations, as opposed to the methods from the related work. We then study the problem of annotating sentences with aspects, and propose a new method that annotates sentences based on a similarity between the aspect signature and the terms in the sentence. The third problem we tackle is the generation of personalized, aspect-based summaries. We propose an optimization algorithm to maximize the coverage of the aspects the user is interested in and the representativeness of sentences in the summary subject to a length and similarity constraints. Finally, we perform three user studies that show that the approach we propose outperforms the state of art method for generating summaries

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Частини книг з теми "Personalized summarization"

Agnihotri, Lalitha, and Nevenka Dimitrova. "Personalized Multimedia Summarization." In Philips Research, 89–111. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0703-9_5.

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Dharan, Nidhin S., and R. Gowtham. "Personalized Abstract Review Summarization Using Personalized Key Information-Guided Network." In Inventive Computation and Information Technologies, 203–16. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6723-7_15.

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Park, Sun. "Personalized Summarization Agent Using Non-negative Matrix Factorization." In PRICAI 2008: Trends in Artificial Intelligence, 1034–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_103.

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Panagiotakis, Costas, Harris Papadakis, and Paraskevi Fragopoulou. "Personalized Video Summarization Based Exclusively on User Preferences." In Lecture Notes in Computer Science, 305–11. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45442-5_38.

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Xu, Hongyan, Hongtao Liu, Wenjun Wang, and Pengfei Jiao. "Neural Adversarial Review Summarization with Hierarchical Personalized Attention." In Database Systems for Advanced Applications, 53–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73197-7_4.

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Li, Yinghui, and Hengbin Yan. "Summarization Exercises and Interpreting Performance in Blended Interpreting Training." In Blended Learning: Educational Innovation for Personalized Learning, 117–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21562-0_10.

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Chen, Fan, and Christophe De Vleeschouwer. "Personalized Summarization of Broadcasted Soccer Videos with Adaptive Fast-Forwarding." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 1–11. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03892-6_1.

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Díaz, Alberto, Pablo Gervás, and Antonio García. "Evaluation of a System for Personalized Summarization of Web Contents." In User Modeling 2005, 453–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527886_63.

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Zhang, Lu, Liangjun Zang, Longtao Huang, Jizhong Han, and Songlin Hu. "An Interactivity-Based Personalized Mutual Reinforcement Model for Microblog Topic Summarization." In Lecture Notes in Computer Science, 518–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97304-3_40.

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Park, Sun. "Personalized Document Summarization Using Non-negative Semantic Feature and Non-negative Semantic Variable." In Lecture Notes in Computer Science, 298–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88906-9_38.

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Тези доповідей конференцій з теми "Personalized summarization"

Agnihotri, Lalitha, John Kender, Nevenka Dimitrova, and John Zimmerman. "Framework for personalized multimedia summarization." In the 7th ACM SIGMM international workshop. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101826.1101841.

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Darabi, Kaveh, and Gheorghita Ghinea. "Personalized video summarization using sift." In SAC 2015: Symposium on Applied Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2695664.2695750.

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Ren, Zhaochun, Shangsong Liang, Edgar Meij, and Maarten de Rijke. "Personalized time-aware tweets summarization." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2484028.2484052.

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Liu, Yong, Xiaolei Wang, Jin Zhang, and Hongbo Xu. "Personalized PageRank Based Multi-document Summarization." In 2008 IEEE International Workshop on Semantic Computing and Systems (WSCS). IEEE, 2008. http://dx.doi.org/10.1109/wscs.2008.32.

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Dong, Yi, Chang Liu, Zhiqi Shen, Yu Han, Zhanning Gao, Pan Wang, Changgong Zhang, Peiran Ren, and Xuansong Xie. "Personalized Video Summarization with Idiom Adaptation." In MM '19: The 27th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3343031.3350584.

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Xiao-Peng Yang and Xiao-Rong Liu. "Personalized multi-document summarization in information retrieval." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4621121.

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Yi, Kun, Yu Guo, Zhi Wang, Lifeng Sun, and Wenwu Zhu. "Personalized Text Summarization Based on Gaze Patterns." In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2020. http://dx.doi.org/10.1109/mipr49039.2020.00070.

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Darabi, Kaveh, and Gheorghita Ghinea. "Personalized video summarization by highest quality frames." In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2014. http://dx.doi.org/10.1109/icmew.2014.6890674.

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Varini, Patrizia, Giuseppe Serra, and Rita Cucchiara. "Personalized Egocentric Video Summarization for Cultural Experience." In ICMR '15: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2671188.2749343.

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Xu, Hongyan, Hongtao Liu, Pengfei Jiao, and Wenjun Wang. "Transformer Reasoning Network for Personalized Review Summarization." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3462854.

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