This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed： the personal name Boolean vectors deno...This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed： the personal name Boolean vectors denoting whether a personal name occurs the text the biographical word Boolean vector representing title, occupation and so forth, and the feature vector with real values. Then, by combining a heuristic strategy based on Boolean vectors with an agglomeratie clustering algorithm based on feature vectors, it seeks to resolve multi-document personal name coreference. Experimental results show that this approach achieves a good performance by testing on ＂Wang Gang＂ corpus.展开更多
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
We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based met...We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].展开更多
This paper reports part of a study to develop a method for automatic multi-document summarization. The current focus is on dissertation abstracts in the field of sociology. The summarization method uses macro-level an...This paper reports part of a study to develop a method for automatic multi-document summarization. The current focus is on dissertation abstracts in the field of sociology. The summarization method uses macro-level and micro-level discourse structure to identify important information that can be extracted from dissertation abstracts, and then uses a variable-based framework to integrate and organize extracted information across dissertation abstracts. This framework focuses more on research concepts and their research relationships found in sociology dissertation abstracts and has a hierarchical structure. A taxonomy is constructed to support the summarization process in two ways： （1） helping to identify important concepts and relations expressed in the text, and （2） providing a structure for linking similar concepts in different abstracts. This paper describes the variable-based framework and the summarization process, and then reports the construction of the taxonomy for supporting the summarization process. An example is provided to show how to use the constructed taxonomy to identify important concepts and integrate the concepts extracted from different abstracts.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
A multi-document summarization method based on Latent Semantic Indexing (LSI) is proposed. The method combines several reports on the same issue into a matrix of terms and sentences, and uses a Singular Value Decompos...A multi-document summarization method based on Latent Semantic Indexing (LSI) is proposed. The method combines several reports on the same issue into a matrix of terms and sentences, and uses a Singular Value Decomposition (SVD) to reduce the dimension of the matrix and extract features, and then the sentence similarity is computed. The sentences are clustered according to similarity of sentences. The centroid sentences are selected from each class. Finally, the selected sentences are ordered to generate the summarization. The evaluation and results are presented, which prove that the proposed methods are efficient.展开更多
Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summar...Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summary is proposed,which has two stages,the acquisition of acandidate sentence set and the optimum selection of sentence.At the first stage,the candidate sentenceset is obtained by redundancy-based sentence selection approach.At the second stage,optimum se-lection of sentences is proposed to delete sentences in the candidate sentence set according to itscontribution to the whole set until getting the appointed summary length.With a test corpus,theROUGE value of summaries gotten by the proposed approach proves its validity,compared with thetraditional method of sentence selection.The influence of the token chosen in the two-stage sentenceselection approach on the quality of the generated summaries is analyzed.展开更多
Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.Ho...Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.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
Non-specific neck pain is a common disease in clinic,and its pathogenesis is not clear.With the progress of the times and the change of living and working habits,the incidence of non-specific neck pain is increasing y...Non-specific neck pain is a common disease in clinic,and its pathogenesis is not clear.With the progress of the times and the change of living and working habits,the incidence of non-specific neck pain is increasing year by year,which has a great impact on people’s physical and mental health,work and life.Traditional Chinese medicine mainly treats non-specific neck pain by acupuncture and massage,while western medicine generally uses exercise and manipulation therapy,but the quality of clinical evidence of all kinds of therapy is not high,which needs to be verified.This paper summarizes the research progress of traditional Chinese medicine and western medicine in the treatment of non-specific neck pain from the aspects of pathogenesis,etiology and pathogenesis of traditional Chinese medicine,and treatment of traditional Chinese medicine and western medicine,so as to provide reference for doctors in clinical treatment of this disease.展开更多
Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeut...Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeutic effects,while traditional Chinese medi‐cine has unique advantages in relieving IBS-D symptoms and preventing recurrence.In recent years,especially with external treatment of traditional Chinese medicine,it has become a new treatment direction in clinical practice and has achieved good therapeutic effects.This article will provide a review of recent research on the treatment of IBS-D using traditional Chinese medicine external treatment methods.展开更多
文摘This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed： the personal name Boolean vectors denoting whether a personal name occurs the text the biographical word Boolean vector representing title, occupation and so forth, and the feature vector with real values. Then, by combining a heuristic strategy based on Boolean vectors with an agglomeratie clustering algorithm based on feature vectors, it seeks to resolve multi-document personal name coreference. Experimental results show that this approach achieves a good performance by testing on ＂Wang Gang＂ corpus.
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.
文摘We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].
文摘This paper reports part of a study to develop a method for automatic multi-document summarization. The current focus is on dissertation abstracts in the field of sociology. The summarization method uses macro-level and micro-level discourse structure to identify important information that can be extracted from dissertation abstracts, and then uses a variable-based framework to integrate and organize extracted information across dissertation abstracts. This framework focuses more on research concepts and their research relationships found in sociology dissertation abstracts and has a hierarchical structure. A taxonomy is constructed to support the summarization process in two ways： （1） helping to identify important concepts and relations expressed in the text, and （2） providing a structure for linking similar concepts in different abstracts. This paper describes the variable-based framework and the summarization process, and then reports the construction of the taxonomy for supporting the summarization process. An example is provided to show how to use the constructed taxonomy to identify important concepts and integrate the concepts extracted from different abstracts.
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A multi-document summarization method based on Latent Semantic Indexing (LSI) is proposed. The method combines several reports on the same issue into a matrix of terms and sentences, and uses a Singular Value Decomposition (SVD) to reduce the dimension of the matrix and extract features, and then the sentence similarity is computed. The sentences are clustered according to similarity of sentences. The centroid sentences are selected from each class. Finally, the selected sentences are ordered to generate the summarization. The evaluation and results are presented, which prove that the proposed methods are efficient.
基金the National Natural Science Foundation of China(No.60575041)the High Technology Researchand Development Program of China(No.2006AA01Z150).
文摘Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summary is proposed,which has two stages,the acquisition of acandidate sentence set and the optimum selection of sentence.At the first stage,the candidate sentenceset is obtained by redundancy-based sentence selection approach.At the second stage,optimum se-lection of sentences is proposed to delete sentences in the candidate sentence set according to itscontribution to the whole set until getting the appointed summary length.With a test corpus,theROUGE value of summaries gotten by the proposed approach proves its validity,compared with thetraditional method of sentence selection.The influence of the token chosen in the two-stage sentenceselection approach on the quality of the generated summaries is analyzed.
基金supported byNationalNatural Science Foundation of China(52274205)and Project of Education Department of Liaoning Province(LJKZ0338).
文摘Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.
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supported by the National Natural Science Foundation of China(No.81603638)the China Postdoctoral Science Foundation(No.2019M662791)+2 种基金the Fundamental Research Funds for the Beijing University of Chinese Medicine(No.2019-JYB-JS-042)the Young Scientist Development Program,Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine(No.DZMYS-201702)Dongcheng District Outstanding Talent Nurturing Program(No.2020-dchrcpyzz-29).
文摘Non-specific neck pain is a common disease in clinic,and its pathogenesis is not clear.With the progress of the times and the change of living and working habits,the incidence of non-specific neck pain is increasing year by year,which has a great impact on people’s physical and mental health,work and life.Traditional Chinese medicine mainly treats non-specific neck pain by acupuncture and massage,while western medicine generally uses exercise and manipulation therapy,but the quality of clinical evidence of all kinds of therapy is not high,which needs to be verified.This paper summarizes the research progress of traditional Chinese medicine and western medicine in the treatment of non-specific neck pain from the aspects of pathogenesis,etiology and pathogenesis of traditional Chinese medicine,and treatment of traditional Chinese medicine and western medicine,so as to provide reference for doctors in clinical treatment of this disease.
文摘Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeutic effects,while traditional Chinese medi‐cine has unique advantages in relieving IBS-D symptoms and preventing recurrence.In recent years,especially with external treatment of traditional Chinese medicine,it has become a new treatment direction in clinical practice and has achieved good therapeutic effects.This article will provide a review of recent research on the treatment of IBS-D using traditional Chinese medicine external treatment methods.
