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Journal articles on the topic 'Generating Summary'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Cheong, Yun-Gyung, Arnav Jhala, Byung-Chull Bae, and R. Young. "Automatically Generating Summary Visualizations from Game Logs." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4, no. 1 (2021): 167–72. http://dx.doi.org/10.1609/aiide.v4i1.18692.

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In this paper we describe a system called ViGLS (Visualization of Game Log Summaries) that generates summaries of gameplay sessions from game logs. ViGLS automatically produces visualization of the summarized actions that are extracted based on cognitive models of summarization. ViGLS is implemented using a service-oriented architecture, de-coupling the summarization methods from any particular game engine being used. The camera code libraries used in visualization are based on constraint based camera control approaches and, in our implementation, make use of the scripting layer of the Unreal

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Gates, Christopher S., Ninghui Li, Hao Peng, et al. "Generating Summary Risk Scores for Mobile Applications." IEEE Transactions on Dependable and Secure Computing 11, no. 3 (2014): 238–51. http://dx.doi.org/10.1109/tdsc.2014.2302293.

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Nakano, Shunsuke, and Takehisa Onisawa. "Generation System of Attractive Summary of Story." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 1 (2008): 41–47. http://dx.doi.org/10.20965/jaciii.2008.p0041.

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This paper proposes a method to generate an attractive summary from a story and describes an attractive summary generation system based on the method. The system consists of two sections. The one is the section extracting an important part of a story, i.e., the part of a new turn of a story defined by the appearance frequency of new words in a story. The other is the section generating a summary from the extracted important part. This section chooses important sentences and deletes unnecessary phrases from the extracted part. Finally, this paper confirms the validity of the presented approach

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Zhao, Shuai, Fucheng You, Wen Chang, Tianyu Zhang, and Man Hu. "Augment BERT with average pooling layer for Chinese summary generation." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1859–68. http://dx.doi.org/10.3233/jifs-211229.

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The BERT pre-trained language model has achieved good results in various subtasks of natural language processing, but its performance in generating Chinese summaries is not ideal. The most intuitive reason is that the BERT model is based on character-level composition, while the Chinese language is mostly in the form of phrases. Directly fine-tuning the BERT model cannot achieve the expected effect. This paper proposes a novel summary generation model with BERT augmented by the pooling layer. In our model, we perform an average pooling operation on token embedding to improve the model’s abilit

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Quiroz, Juan C., Liliana Laranjo, Ahmet Baki Kocaballi, et al. "Identifying relevant information in medical conversations to summarize a clinician-patient encounter." Health Informatics Journal 26, no. 4 (2020): 2906–14. http://dx.doi.org/10.1177/1460458220951719.

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To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and

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Rana, Deepak Singh. "Generating Document Summary using Data Mining and Clustering Techniques." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 285–92. http://dx.doi.org/10.17762/msea.v70i1.2310.

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    Abstract This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality sum

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Lee, Inkyoun, and Dongsu Kang. "Generating Metamorphic Test Cases for Transformer-based Korean Summary." KIISE Transactions on Computing Practices 29, no. 11 (2023): 509–17. http://dx.doi.org/10.5626/ktcp.2023.29.11.509.

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Zhao, Hongtao. "Overview of Judicial Text Summarization Method." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 945–56. http://dx.doi.org/10.54097/qe1xts44.

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This article delves deep into the core aspects of the task of generating judicial text summaries. Through a systematic review and distillation of existing relevant literature, the article primarily focuses on extractive text summarization techniques in both unsupervised and supervised learning contexts, conducting a multidimensional and comprehensive analysis. To begin with, the article traces the evolution of text summarization techniques and dissects the differences between extractive and generative text summarization methods, along with a comparison of various algorithms. Furthermore, it pr

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Singh, Sanjay, Srinivasa Murali Dunga, AS Mandal, Chandra Shekhar, and Santanu Chaudhury. "FPGA Based Embedded Implementation of Video Summary Generation Scheme in Smart Camera." Advanced Materials Research 403-408 (November 2011): 516–21. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.516.

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In any remote surveillance scenario, smart cameras have to take intelligent decisions to generate summary frames to minimize communication and processing overhead. Video summary generation, in the context of smart camera, is the process of merging the information from multiple frames. A summary generation scheme based on clustering based change detection algorithm has been implemented in our smart camera system for generating frames to deliver requisite information. In this paper we propose an embedded platform based framework for implementing summary generation scheme using HW-SW Co-Design ba

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Konarieva, I., D. Pydorenko, and O. Turuta. "A SURVEY OF METHODS OF TEXT-TO-IMAGE TRANSLATION." Bionics of Intelligence 2, no. 93 (2019): 64–68. http://dx.doi.org/10.30837/bi.2019.2(93).11.

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The given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to tra

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Krauss, Oliver, Barbara Franz, and Andreas Schuler. "Automated On-Demand Generation Of Patient Summary Documents." International Journal of Electronics and Telecommunications 61, no. 2 (2015): 151–57. http://dx.doi.org/10.1515/eletel-2015-0019.

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Abstract Patient summary documents provide crucial information about a patient, like allergies and adverse reactions, which are necessary for an efficient and safe treatment and offer a quick overview of the patients health status. Automatically generating patient summaries from Electronic Health Records (EHR) reduces the workload of medical personnel. Nevertheless, existing approaches do not take several challenges that occur in live operation into account. Based on a health standard-compliant approach, a system for on-demand generation of patient summaries was implemented and evaluated using

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Manju, K., S. David Peter, and Sumam Idicula. "A Framework for Generating Extractive Summary from Multiple Malayalam Documents." Information 12, no. 1 (2021): 41. http://dx.doi.org/10.3390/info12010041.

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Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduc

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GIANNESSI, LUCA. "SUMMARY OF WORKING GROUP 3: NEW CHALLENGES IN THEORY AND MODELING." International Journal of Modern Physics A 22, no. 23 (2007): 4198–203. http://dx.doi.org/10.1142/s0217751x07037755.

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Working group three was devoted to contributions on theory and modeling of devices and new ideas, aiming at increasing the beam brightness and at generating radiation at short wavelengths. The contributions presented span from the beam dynamics in radiofrequency accelerators, emittance compensation schemes and optimization of the beam brightness at low energy, analysis of the emittance dilution processes during acceleration and compression, laser plasma sources and the generation of radiation in FELs and other radiation sources. Analysis specifically oriented to the modeling of the beam and ra

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Saggion, Horacio, and Guy Lapalme. "Generating Indicative-Informative Summaries with SumUM." Computational Linguistics 28, no. 4 (2002): 497–526. http://dx.doi.org/10.1162/089120102762671963.

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We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method wa

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Miura, Shizuka, and Atsushi Suzuki. "Brief summary of the current protocols for generating intestinal organoids." Development, Growth & Differentiation 60, no. 6 (2018): 387–92. http://dx.doi.org/10.1111/dgd.12559.

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Zhenxuan, Zhang, and Peng Minjing. "Automatic summary generating technology of vegetable traceability for information sharing." IOP Conference Series: Earth and Environmental Science 69 (June 2017): 012068. http://dx.doi.org/10.1088/1755-1315/69/1/012068.

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Ye, Jintao, Zhao Yan Ming, and Tat Seng Chua. "Generating Incremental Length Summary Based on Hierarchical Topic Coverage Maximization." ACM Transactions on Intelligent Systems and Technology 7, no. 3 (2016): 1–33. http://dx.doi.org/10.1145/2809433.

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Kong, Hyesoo, and Wooju Kim. "Generating summary sentences using Adversarially Regularized Autoencoders with conditional context." Expert Systems with Applications 130 (September 2019): 1–11. http://dx.doi.org/10.1016/j.eswa.2019.04.014.

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Wise, John Pierce, Richard N. Winn, and J. Larry Renfro. "Generating new marine cell lines and transgenic species?conference summary." Journal of Experimental Zoology 292, no. 3 (2002): 217–20. http://dx.doi.org/10.1002/jez.10030.

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Nagwani, Naresh Kumar, and Shrish Verma. "Generating Intelligent Summary Terms for Improving Knowledge Discovery in Software Bug Repositories." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (2016): 827–44. http://dx.doi.org/10.1142/s0218194016500273.

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Software bug records are stored and managed using bug tracking tools. A software bug is characterized by a number of attributes like bug id, opened date, closed date, reported by, assigned to, summary (title), description and set of comments. Summary and description are the two important attributes of a bug. Description gives the detailed information about a bug, whereas summary (title) of a bug gives a quick glance and short information about a bug. The objective of this study is to discover the relationship between description and summary attributes of a bug and to find whether summary of a

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Nandy, Abhilash, and Sambaran Bandyopadhyay. "Language Models of Code Are Few-Shot Planners and Reasoners for Multi-Document Summarization with Attribution." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24930–38. https://doi.org/10.1609/aaai.v39i23.34676.

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Document summarization has greatly benefited from advances in large language models (LLMs). In real-world situations, summaries often need to be generated from multiple documents with diverse sources and authors, lacking a clear information flow. Naively concatenating these documents and generating a summary can lead to poorly structured narratives and redundancy. Additionally, attributing each part of the generated summary to a specific source is crucial for reliability. In this study, we address multi-document summarization with attribution using our proposed solution ***MiDAS-PRo***, consis

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SARAVANAN, M., S. RAMAN, and B. RAVINDRAN. "A PROBABILISTIC APPROACH TO MULTI-DOCUMENT SUMMARIZATION FOR GENERATING A TILED SUMMARY." International Journal of Computational Intelligence and Applications 06, no. 02 (2006): 231–43. http://dx.doi.org/10.1142/s1469026806001976.

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Data availability is not a major issue at present times in view of the widespread use of Internet; however, information and knowledge availability are the issues. Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz's K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. Highly ranked sentences are selected for the final summary. The

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Bandari, Sumalatha, and Vishnu Vardhan Bulusu. "BERT Tokenization and Hybrid-Optimized Deep Recurrent Neural Network for Hindi Document Summarization." International Journal of Fuzzy System Applications 11, no. 1 (2022): 1–28. http://dx.doi.org/10.4018/ijfsa.313601.

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Text summarization generates a concise summary of the available information by determining the most relevant and important sentences in the document. In this paper, an effective approach of document summarization is developed for generating summary of Hindi documents. The developed deep learning-based Hindi document summarization system comprises of a number of phases, such as input data acquisition, tokenization, feature extraction, score generation, and sentence extraction. Here, a deep recurrent neural network (Deep RNN) is employed for generating the scores of the sentences based on the si

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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 (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-

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Rahman, M. Arif, Sarah M. Preum, Ronald Williams, Homa Alemzadeh, and John A. Stankovic. "GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13356–62. http://dx.doi.org/10.1609/aaai.v34i08.7049.

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EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary rep

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Song, Kaiqiang, Logan Lebanoff, Qipeng Guo, et al. "Joint Parsing and Generation for Abstractive Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8894–901. http://dx.doi.org/10.1609/aaai.v34i05.6419.

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Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged. The proposed method thus holds promise for producing grammatical sentences and encouraging the summary to stay true-to-original. Our contributions of this work are twofold. First, we present a nov

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Zhong, Xiaopin, Junwei Zhu, Weixiang Liu, Chongxin Hu, Yuanlong Deng, and Zongze Wu. "An Overview of Image Generation of Industrial Surface Defects." Sensors 23, no. 19 (2023): 8160. http://dx.doi.org/10.3390/s23198160.

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Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The perf

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Frey, Nancy, Douglas Fisher, and Ted Hernandez. "What’s the Gist? Summary Writing for Struggling Adolescent Writers." Voices from the Middle 11, no. 2 (2003): 43–49. http://dx.doi.org/10.58680/vm20033069.

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The ability to write tight, concise, and accurate summaries of texts is often a struggle for our students. With specific focus on prÃ©cis writing, the authors used the GIST (Generating Interaction between Schemata and Text) learning strategy to increase comprehension of expository texts. By breaking down texts into logical sections and writing one–sentence summaries of each, students were able to produce well–written summaries. A rubric for summary assessment is included.

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Manimozhi, K., V. Kalaichelvi, M. Poornima, and A. Sumathi. "An Approach for Text Steganography: Generating Tamil Text Summary Using Tamil Phonetics." International Review on Computers and Software (IRECOS) 10, no. 2 (2015): 137. http://dx.doi.org/10.15866/irecos.v10i2.5196.

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Majumder, Anandaprova, and Suvamoy Changder. "A Novel Approach for Text Steganography: Generating Text Summary Using Reflection Symmetry." Procedia Technology 10 (2013): 112–20. http://dx.doi.org/10.1016/j.protcy.2013.12.343.

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Howlader, Prottyee, Prapti Paul, Meghana Madavi, Laxmi Bewoor, and V. S. Deshpande. "Fine Tuning Transformer Based BERT Model for Generating the Automatic Book Summary." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 347–52. http://dx.doi.org/10.17762/ijritcc.v10i1s.5902.

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Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. Abstractive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of atten

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Chitra.P1, Baskaran.R2 Sarukesi.K3. "QUERY SENSITIVE COMPARATIVE SUMMARIZATION OF SEARCH RESULTS USING CONCEPT BASED SEGMENTATION." COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ) 1, no. 5 (2019): 31–43. https://doi.org/10.5281/zenodo.3382025.

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Query sensitive summarization aims at providing the users with the summary of the contents of single or multiple web pages based on the search query. This paper proposes a novel idea of generating a comparative summary from a set of URLs from the search result. User selects a set of web page links from the search result produced by search engine. Comparative summary of these selected web sites is generated. This method makes use of HTML DOM tree structure of these web pages. HTML documents are segmented into set of concept blocks. Sentence score of each concept block is computed with respect t

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Ting, D. Y., M. Healey, S. R. Lipsitz, et al. "Physician Perceptions and Beliefs about Generating and Providing a Clinical Summary of the Office Visit." Applied Clinical Informatics 06, no. 03 (2015): 577–90. http://dx.doi.org/10.4338/aci-2015-04-ra-0043.

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Summary Background: A core measure of the meaningful use of EHR incentive program is the generation and provision of the clinical summary of the office visit, or the after visit summary (AVS), to patients. However, little research has been conducted on physician perceptions and beliefs about the AVS. Objective: Evaluate physician perceptions and beliefs about the AVS and the effect of the AVS on workload, patient outcomes, and the care the physician delivers. Methods: A cross-sectional online survey of physicians at two academic medical centers (AMCs) in the northeast who are participating in

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Zhou, Zijian, Jibin Song, Liming Nie, and Xiaoyuan Chen. "Reactive oxygen species generating systems meeting challenges of photodynamic cancer therapy." Chemical Society Reviews 45, no. 23 (2016): 6597–626. http://dx.doi.org/10.1039/c6cs00271d.

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Parikh, Soham, Quaizar Vohra, and Mitul Tiwari. "Automated Utterance Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13344–49. http://dx.doi.org/10.1609/aaai.v34i08.7047.

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Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need

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Glenc, Piotr. "Narzędzia do automatycznego streszczania tekstów w języku polskim. Stan badań naukowych i prac wdrożeniowych." e-mentor 89, no. 2 (2021): 67–77. http://dx.doi.org/10.15219/em89.1513.

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The goal of the publication is to present the state of research and works carried out in Poland on the issue of automatic text summarization. The author describes principal theoretical and methodological issues related to automatic summary generation followed by the outline of the selected works on the automatic abstracting of Polish texts. The author also provides three examples of IT tools that generate summaries of texts in Polish (Summarize, Resoomer, and NICOLAS) and their characteristics derived from the conducted experiment, which included quality assessment of generated summaries using

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Price, J. R., O. Jimenez, L. Faulder, B. Edwards, and V. Parthasarathy. "Ceramic Stationary Gas Turbine Development Program—Fifth Annual Summary." Journal of Engineering for Gas Turbines and Power 121, no. 4 (1999): 586–92. http://dx.doi.org/10.1115/1.2818512.

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A program is being performed under the sponsorship of the United States Department of Energy, Office of Industrial Technologies, to improve the performance of stationary gas turbines in cogeneration through the selective replacement of metallic hot section components with ceramic parts. The program focuses on design, fabrication, and testing of ceramic components, generating a materials properties data base, and applying life prediction and nondestructive evaluation (NDE). The development program is being performed by a team led by Solar Turbines Incorporated, and which includes suppliers of c

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Helen, Afrida. "Automatic Abstractive Summarization Task for New Article." EMITTER International Journal of Engineering Technology 6, no. 1 (2018): 22–34. http://dx.doi.org/10.24003/emitter.v6i1.212.

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Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary w

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Wang, Mingyang, Dongtian Leng, Jinjin Ren, and Peng Yu. "Generating a Citation Summary Based on Cited Sentences and the Implied Citation Emotions." IEEE Access 9 (2021): 18042–51. http://dx.doi.org/10.1109/access.2021.3054740.

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Pleil, Joachim D. "QQ-plots for assessing distributions of biomarker measurements and generating defensible summary statistics." Journal of Breath Research 10, no. 3 (2016): 035001. http://dx.doi.org/10.1088/1752-7155/10/3/035001.

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Chen, Shyi-Ming, and Ming-Hung Huang. "Automatically generating the weather news summary based on fuzzy reasoning and ontology techniques." Information Sciences 279 (September 2014): 746–63. http://dx.doi.org/10.1016/j.ins.2014.04.027.

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Xiong, Yu, Xiangmin Zhou, Yifei Zhang, Shi Feng, and Daling Wang. "Cross the data desert: generating textual-visual summary on the evolutionary microblog stream." Multimedia Tools and Applications 78, no. 6 (2018): 6409–40. http://dx.doi.org/10.1007/s11042-018-6297-6.

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Qazvinian, V., D. R. Radev, S. M. Mohammad, et al. "Generating Extractive Summaries of Scientific Paradigms." Journal of Artificial Intelligence Research 46 (February 20, 2013): 165–201. http://dx.doi.org/10.1613/jair.3732.

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Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Ne

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Harris, Jonathan J., Ching-Hua Chen, and Mohammed J. Zaki. "A Framework for Generating Summaries from Temporal Personal Health Data." ACM Transactions on Computing for Healthcare 2, no. 3 (2021): 1–43. http://dx.doi.org/10.1145/3448672.

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Although it has become easier for individuals to track their personal health data (e.g., heart rate, step count, and nutrient intake data), there is still a wide chasm between the collection of data and the generation of meaningful summaries to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work toward striving closer to their health goals. We aim to bridge the gap between data collection and summary generation by mining the data for interesting behavioral findings that may p

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Steinbuss, Georg, and Klemens Böhm. "Generating Artificial Outliers in the Absence of Genuine Ones — A Survey." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (2021): 1–37. http://dx.doi.org/10.1145/3447822.

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By definition, outliers are rarely observed in reality, making them difficult to detect or analyze. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers or with benchmarking outlier-detection algorithms. The literature features different approaches to generate artificial outliers. However, systematic comparison of these approaches remains absent. This surveys and compares these approaches. We start by clarifying the terminology in the field, which varies from publication to publication, and we propose a general problem formula

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She, Shuaijie, Xiang Geng, Shujian Huang, and Jiajun Chen. "CoP: Factual Inconsistency Detection by Controlling the Preference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13556–63. http://dx.doi.org/10.1609/aaai.v37i11.26589.

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Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation model reflect its preferences for the generated summary, including the preference for factual consistency, and the preference for the language or knowledge prior as well. To separate the preference for factual consistency, we propose an unsupervised framework named CoP by contro

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Alsharman, Nesreen Mohammad, and Inna V. Pivkina. "Generating Summaries Through Unigram and Bigram." International Journal of Information Technology and Web Engineering 15, no. 1 (2020): 64–74. http://dx.doi.org/10.4018/ijitwe.2020010105.

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This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesse

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YAMAMOTO, KAZUHIDE, SHIGERU MASUYAMA, and SHOZO NAITO. "GREEN: An Experimental System Generating Summary of Japanese Editorials by Combining Multiple Discourse Characteristics." Journal of Natural Language Processing 2, no. 1 (1995): 39–55. http://dx.doi.org/10.5715/jnlp.2.39.

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Duffourc, Mindy Nunez, and Sara Gerke. "Generative AI in Health Care and Liability Risks for Physicians and Safety Concerns for Patients." JAMA 330, no. 4 (2023): 313–14. https://doi.org/10.1001/jama.2023.9630.

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<strong>Plain language summary:</strong> This Viewpoint discusses the potential use of generative artificial intelligence (AI) in medical care and the liability risks for physicians using the technology, as well as offers suggestions for safeguards to protect patients. <strong>Generative artificial intelligence (AI) </strong>is a quickly emerging subfield of AI that can be trained with large data sets to create realistic images, videos, text, sound, 3-dimensional models, virtual environments, and even drug compounds. It has gained more attention recently as chatbots such as OpenAI’s Chat

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Deng, Yang, Wai Lam, Yuexiang Xie, et al. "Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7651–58. http://dx.doi.org/10.1609/aaai.v34i05.6266.

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Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essent

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