Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss abo...Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.展开更多
Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good res...Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.展开更多
This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main c...This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated sum- maries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.展开更多
Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient heal...Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.展开更多
Objective: To search, evaluate and integrate the best domestic and foreign evidence on the preoperative pre-rehabilitation of lung cancer patients with COPD, and summarize the best evidence. Methods: Cochrane Library,...Objective: To search, evaluate and integrate the best domestic and foreign evidence on the preoperative pre-rehabilitation of lung cancer patients with COPD, and summarize the best evidence. Methods: Cochrane Library, BMJ Best Practice, JBI Center for International Cooperation in Evidence-Based Health Care Library, and Up to were searched by computer system Date, Embase, CINAHL, PubMed, Evidence-Based Health Care Center Database (JBI), American Guidenet, Global Guidenet, CNKI, CMBM, Wanfang, and VIP databases on preoperative pre-rehabilitation of patients with lung cancer and COPD. Literature screening and quality evaluation were conducted independently by two researchers, and evidence was extracted, summarized and summarized according to the theme. Results: A total of 13 kinds of literature were included, including 4 guidelines, 4 systematic reviews, 3 evidence summaries, and 2 expert consensus articles. This paper summarizes 44 best evidence on pre-rehabilitation of lung cancer patients with COPD, including 8 aspects: pre-rehabilitation content and principle, pre-rehabilitation assessment, exercise rehabilitation, nutrition management, psychological guidance, health education, multidisciplinary cooperation, quality control and follow-up. Conclusion: The best evidence of preoperative pre-rehabilitation for patients with lung cancer complicated with COPD is rich in content, and needs to be selected according to the characteristics of the hospital and the clinical environment. Some of the evidence still needs to be confirmed by higher-quality studies. .展开更多
Integrated writing tasks,requiring writers to summarize source content and integrate their ideas or experience with those in the source text,are widely used in standardized tests of English proficiency.Integrated writ...Integrated writing tasks,requiring writers to summarize source content and integrate their ideas or experience with those in the source text,are widely used in standardized tests of English proficiency.Integrated writing adds an element not found in traditional independent writing:the use of source text material.Recently,concern has been raised that over-reliance on source texts in integrated writing may lead to plagiarism as well as inaccurate assessment which calls for further investigation.Research on L2 students’writing using sources has broadened its focus from transgressive citing practices to stages of skill development in this complicated literacy,the successive challenges students face,and how instruction might be of benefit.Previous literature mainly focused on source use in academic writing and seldom considered summary writing in standardized tests,while summary strategy is particularly important in standardized tests,in which summary accounts for a significant part of the assessment criteria.Given that a series of previous literature have indicated that source text use,source texts,and prompts are associated with the quality of summary writing,this article reviews the source text borrowing in the summary of integrated writing to further explore the relationship between some variables and the quality of summary writing and therefore finds some research gaps in this field.展开更多
Objective: To summarize the best evidence for the evaluation and management of chemotherapy-related nausea and vomiting in cancer patients, so as to promote the standardized management of chemotherapy-related nausea a...Objective: To summarize the best evidence for the evaluation and management of chemotherapy-related nausea and vomiting in cancer patients, so as to promote the standardized management of chemotherapy-related nausea and vomiting in cancer patients. Methods: A computer search was conducted for all evidence on chemotherapy-associated nausea and vomiting interventions, including guidelines, expert consensus, best clinical practice information booklet, recommended practice, evidence summary, and systematic review. The search period is from April 30, 2022. After the literature quality evaluation, the evidence extraction and summary of the literature meeting the quality standards. Results: A total of 16 literatures were included, including 9 guidelines, 5 systematic reviews and 2 expert consensus papers. 46 pieces of best evidence on the assessment and management of chemotherapy related nausea and vomiting in cancer patients were summarized, including 6 aspects including risk assessment and management, non-drug management, drug management, multidisciplinary cooperation, education and training, and health education. Conclusion: This study summarized the current best evidence on the assessment and management of chemotherapy-related nausea and vomiting in cancer patients. Clinical staff should apply evidence according to specific clinical scenarios, professional skills and patients’ wishes in order to reduce the degree and incidence of chemotherapy-related nausea and vomiting in cancer patients and improve the quality of care.展开更多
目的总结成人患者安全输注血管活性药物的最佳证据总结,为临床护理工作提供证据支持。方法检索UpToDate、BMJ Best Practice、澳大利亚乔安娜布里格斯研究所循证卫生保健中心数据库、Cochrane Library、美国国立指南库、美国疾病预防与...目的总结成人患者安全输注血管活性药物的最佳证据总结,为临床护理工作提供证据支持。方法检索UpToDate、BMJ Best Practice、澳大利亚乔安娜布里格斯研究所循证卫生保健中心数据库、Cochrane Library、美国国立指南库、美国疾病预防与控制中心、英国国家临床医学研究所指南库、国际指南协作网、CINAHL、Embase、加拿大安大略注册护士协会、英国重症监护协会、美国静脉输液护理学会、PubMed、Web of Science、中国知网、万方数据库、维普网、中华护理学会、中国生物医学文献数据库和医脉通中有关成人患者血管活性药物输注的指南、专家共识、团体标准、系统评价、证据总结和原始研究等证据。检索时限为2013年1月1日-2023年3月10日。由2名具有循证护理研究背景的研究者独立进行文献质量评价及证据筛查,证据汇总后由团队归类综合。结果共纳入12篇文献,从培训与教育、评估、血管通路选择、输注方案、并发症管理5个维度总结了29条证据。结论该研究总结了成人患者血管活性药物输注的最佳证据,可为医务人员提供科学的输注方案,保证患者安全,提高护理质量。展开更多
目的检索、评价并整合国内外眼科超声乳化手柄清洗质量管理的相关证据,为临床实践提供指导。方法计算机检索国际指南协作网、美国国立指南网、BMJ Best Practice、PubMed、万方数据库、中国知网等网站或数据库中关于眼科超声乳化手柄清...目的检索、评价并整合国内外眼科超声乳化手柄清洗质量管理的相关证据,为临床实践提供指导。方法计算机检索国际指南协作网、美国国立指南网、BMJ Best Practice、PubMed、万方数据库、中国知网等网站或数据库中关于眼科超声乳化手柄清洗质量管理的所有证据,包括指南、专家共识、系统评价及证据总结,检索时限为从建库至2023年2月15日。结果共纳入10篇文献,其中实践指南4篇,专家共识4篇,系统评价1篇,证据总结1篇。总结了管理要求、环境要求、设备设施与耗材要求、人员要求、操作要求、现场预处理、回收转运、清洗及清洗质量评价9个方面共38条证据。结论该研究总结了眼科超声乳化手柄清洗质量管理的最佳证据,为规范超声乳化手柄的清洗质量管理提供了循证依据,可为提高眼科器械清洗质量水平、预防与控制医院感染发生提供参考。展开更多
文摘Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.
基金This work was supported in part by the National Science Foundation Project of P.R.China 484 under Grant No.52071349partially supported by Young and Middle-aged Talents Project of the State Ethnic Affairs 487 Commission.
文摘Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.
文摘This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated sum- maries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.
基金This work was supported by the National Natural Science Foundation of China(Grant No.U1811262).
文摘Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.
文摘Objective: To search, evaluate and integrate the best domestic and foreign evidence on the preoperative pre-rehabilitation of lung cancer patients with COPD, and summarize the best evidence. Methods: Cochrane Library, BMJ Best Practice, JBI Center for International Cooperation in Evidence-Based Health Care Library, and Up to were searched by computer system Date, Embase, CINAHL, PubMed, Evidence-Based Health Care Center Database (JBI), American Guidenet, Global Guidenet, CNKI, CMBM, Wanfang, and VIP databases on preoperative pre-rehabilitation of patients with lung cancer and COPD. Literature screening and quality evaluation were conducted independently by two researchers, and evidence was extracted, summarized and summarized according to the theme. Results: A total of 13 kinds of literature were included, including 4 guidelines, 4 systematic reviews, 3 evidence summaries, and 2 expert consensus articles. This paper summarizes 44 best evidence on pre-rehabilitation of lung cancer patients with COPD, including 8 aspects: pre-rehabilitation content and principle, pre-rehabilitation assessment, exercise rehabilitation, nutrition management, psychological guidance, health education, multidisciplinary cooperation, quality control and follow-up. Conclusion: The best evidence of preoperative pre-rehabilitation for patients with lung cancer complicated with COPD is rich in content, and needs to be selected according to the characteristics of the hospital and the clinical environment. Some of the evidence still needs to be confirmed by higher-quality studies. .
文摘Integrated writing tasks,requiring writers to summarize source content and integrate their ideas or experience with those in the source text,are widely used in standardized tests of English proficiency.Integrated writing adds an element not found in traditional independent writing:the use of source text material.Recently,concern has been raised that over-reliance on source texts in integrated writing may lead to plagiarism as well as inaccurate assessment which calls for further investigation.Research on L2 students’writing using sources has broadened its focus from transgressive citing practices to stages of skill development in this complicated literacy,the successive challenges students face,and how instruction might be of benefit.Previous literature mainly focused on source use in academic writing and seldom considered summary writing in standardized tests,while summary strategy is particularly important in standardized tests,in which summary accounts for a significant part of the assessment criteria.Given that a series of previous literature have indicated that source text use,source texts,and prompts are associated with the quality of summary writing,this article reviews the source text borrowing in the summary of integrated writing to further explore the relationship between some variables and the quality of summary writing and therefore finds some research gaps in this field.
文摘Objective: To summarize the best evidence for the evaluation and management of chemotherapy-related nausea and vomiting in cancer patients, so as to promote the standardized management of chemotherapy-related nausea and vomiting in cancer patients. Methods: A computer search was conducted for all evidence on chemotherapy-associated nausea and vomiting interventions, including guidelines, expert consensus, best clinical practice information booklet, recommended practice, evidence summary, and systematic review. The search period is from April 30, 2022. After the literature quality evaluation, the evidence extraction and summary of the literature meeting the quality standards. Results: A total of 16 literatures were included, including 9 guidelines, 5 systematic reviews and 2 expert consensus papers. 46 pieces of best evidence on the assessment and management of chemotherapy related nausea and vomiting in cancer patients were summarized, including 6 aspects including risk assessment and management, non-drug management, drug management, multidisciplinary cooperation, education and training, and health education. Conclusion: This study summarized the current best evidence on the assessment and management of chemotherapy-related nausea and vomiting in cancer patients. Clinical staff should apply evidence according to specific clinical scenarios, professional skills and patients’ wishes in order to reduce the degree and incidence of chemotherapy-related nausea and vomiting in cancer patients and improve the quality of care.
文摘目的总结成人患者安全输注血管活性药物的最佳证据总结,为临床护理工作提供证据支持。方法检索UpToDate、BMJ Best Practice、澳大利亚乔安娜布里格斯研究所循证卫生保健中心数据库、Cochrane Library、美国国立指南库、美国疾病预防与控制中心、英国国家临床医学研究所指南库、国际指南协作网、CINAHL、Embase、加拿大安大略注册护士协会、英国重症监护协会、美国静脉输液护理学会、PubMed、Web of Science、中国知网、万方数据库、维普网、中华护理学会、中国生物医学文献数据库和医脉通中有关成人患者血管活性药物输注的指南、专家共识、团体标准、系统评价、证据总结和原始研究等证据。检索时限为2013年1月1日-2023年3月10日。由2名具有循证护理研究背景的研究者独立进行文献质量评价及证据筛查,证据汇总后由团队归类综合。结果共纳入12篇文献,从培训与教育、评估、血管通路选择、输注方案、并发症管理5个维度总结了29条证据。结论该研究总结了成人患者血管活性药物输注的最佳证据,可为医务人员提供科学的输注方案,保证患者安全,提高护理质量。
文摘目的检索、评价并整合国内外眼科超声乳化手柄清洗质量管理的相关证据,为临床实践提供指导。方法计算机检索国际指南协作网、美国国立指南网、BMJ Best Practice、PubMed、万方数据库、中国知网等网站或数据库中关于眼科超声乳化手柄清洗质量管理的所有证据,包括指南、专家共识、系统评价及证据总结,检索时限为从建库至2023年2月15日。结果共纳入10篇文献,其中实践指南4篇,专家共识4篇,系统评价1篇,证据总结1篇。总结了管理要求、环境要求、设备设施与耗材要求、人员要求、操作要求、现场预处理、回收转运、清洗及清洗质量评价9个方面共38条证据。结论该研究总结了眼科超声乳化手柄清洗质量管理的最佳证据,为规范超声乳化手柄的清洗质量管理提供了循证依据,可为提高眼科器械清洗质量水平、预防与控制医院感染发生提供参考。