Chinese multi-document personal name disambiguation

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.

Using AdaBoost Meta-Learning Algorithm for Medical News Multi-Document Summarization

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.

Density peaks clustering based integrate framework for multi-document summarization

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].

Constructing a taxonomy to support multi-document summarization of dissertation abstracts

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.

Unsupervised Graph-Based Tibetan Multi-Document Summarization

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.

Research on multi-document summarization based on latent semantic indexing

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.

TWO-STAGE SENTENCE SELECTION APPROACH FOR MULTI-DOCUMENT SUMMARIZATION

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.

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

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.

Multi-Document Summarization Model Based on Integer Linear Programming

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.

Current Research Status of Traditional Chinese Medicine External Treatment for Diarrhea Type Irritable Bowel Syndrome

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.

Research progress of non-specific neck pain in traditional Chinese medicine and western medicine

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.

中医药治疗广泛性焦虑症的研究进展

广泛性焦虑症(generalized anxiety disorder,GAD)是一种慢性且普遍的焦虑性疾病,以精神性焦虑为核心,以过度焦虑为主要症状。同时GAD对外界刺激敏感,伴有自主神经功能紊乱,典型症状以躯体性焦虑、运动性不安、肌肉紧张、自主神经功能紊乱等为主,伴随疲倦、惊恐等症状。近些年,GAD发病率总体呈上升趋势,关于GAD的研究已从众多方面展开。本文简要整理了近年来有关GAD的中医疗法,主要涉及中医外治法和中医内治法,外治法包括针灸、刮痧、推拿、耳穴贴压、穴位贴敷、中医情志疗法等,内治法主要为中药治疗法。

中医适宜技术在成人癌痛患者中应用的研究进展

对适用于成人癌痛患者的中医适宜技术进行综述,旨在为临床开展适合癌痛患者的中医适宜技术提供参考,为后续的相关研究提供依据和方向。

中医药治疗糖尿病肾脏病的现状及研究进展

糖尿病肾脏病(DKD)是糖尿病常见的微血管并发症之一,威胁着人类的生命健康。对中医、西医在DKD的发病机理及治疗方面的研究进展进行总结,发现中医和西医在DKD治疗中各有利弊,但中西药联合治疗能明显提高DKD的治疗效果,并能有效干预肾实质病变,延缓病情发展。

基于多策略强化学习的低资源跨语言摘要方法研究

(CLS)旨在给定1个源语言文件(如越南语),生成目标语言(如中文)的摘要。端到端的CLS模型在大规模、高质量的标记数据基础上取得较优的性能,这些标记数据通常是利用机器翻译模型将单语摘要语料库翻译成CLS语料库而构建的。然而,由于低资源语言翻译模型的性能受限,因此翻译噪声会被引入到CLS语料库中,导致CLS模型性能降低。提出基于多策略的低资源跨语言摘要方法。利用多策略强化学习解决低资源噪声训练数据场景下的CLS模型训练问题,引入源语言摘要作为额外的监督信号来缓解翻译后的噪声目标摘要影响。通过计算源语言摘要和生成目标语言摘要之间的单词相关性和单词缺失程度来学习强化奖励,在交叉熵损失和强化奖励的约束下优化CLS模型。为验证所提模型的性能,构建1个有噪声的汉语-越南语CLS语料库。在汉语-越南语和越南语-汉语跨语言摘要数据集上的实验结果表明,所提模型ROUGE分数明显优于其他基线模型,相比NCLS基线模型,该模型ROUGE-1分别提升0.71和0.84,能够有效弱化噪声干扰,从而提高生成摘要的质量。

中医药治疗慢性肾衰竭的研究进展

经过查阅相关资料和整理临床材料,发现中医药治疗慢性肾衰竭优势显著,集适应证广、灵活安全、医疗成本低、不良反应小于一体。一方面,能够延缓肾功能的减退,推延进入或不进入肾脏替代疗法,改善预后;另一方面,减少毒素堆积,调节肠道菌群,减轻并发症的发生。文章就近年来研究中医药医治慢性肾衰竭的进展作一综述。

融合图像信息的越汉跨语言新闻文本摘要方法

[目的]为了有效剔除冗余文本信息,提高摘要简洁性同时充分利用图像信息提高摘要准确性,对融合图像信息的越汉跨语言新闻文本摘要方法进行研究.[方法]首先利用文本编码器和图像编码器对越南语新闻文本和图像进行表征,其次利用图文对比损失增强图像和文本表征的一致性,迫使越南语的表征空间趋近于与语言无关的图像表征空间,然后利用图文融合器进行图像和文本的有效融合,增强新闻文本的关键信息提取能力,最后利用摘要解码器生成中文摘要.[结果]在本文构建的越汉多模态跨语言摘要数据集上,相较于对比方法,本方法生成的摘要具备更高的ROUGE分数、信息量、简洁度和流畅度.[结论]引入图像信息有利于生成高质量的跨语言摘要;采用单任务直接学习两种语言的互动信息可以降低将跨语言摘要分解为多任务带来的误差累积.

基于二阶段对比学习的中文自动文本摘要方法研究

在中文自动文本摘要中,暴露偏差是一个常见的现象。由于中文文本自动摘要在序列到序列模型训练时解码器每一个词输入都来自真实样本,但是在测试时当前输入用的却是上一个词的输出,导致预测词在训练和测试时是从不同的分布中推断出来的,而这种不一致将导致训练模型和测试模型直接的差异。本文提出了一个两阶段对比学习框架以实现面向中文文本的生成式摘要训练,同时从摘要模型的训练以及摘要评价的建模进行对比学习。在大规模中文短文本摘要数据集(LCSTS)以及自然语言处理与中文计算会议的文本数据集(NLPCC)上的实验结果表明,相比于基线模型,本文方法可以获得更高的面向召回率的摘要评价方法(ROUGE)指标,并能更好地解决暴露偏差问题。

藏汉跨语言摘要数据集TiCLS

是自然语言处理领域中的重要研究方向,旨在源语言的文本上生成目标语言的摘要,帮助人们更好地理解和传播不同语言之间的信息。近年来,随着深度学习和预训练技术的发展,跨语言摘要任务在高资源语言数据上取得了显著的进展。然而藏文等低资源语言由于可用的数据稀少,藏汉跨语言摘要研究还处于起步阶段。为了推动藏汉跨语言摘要的研究,本研究构建了可用于藏汉跨语言摘要生成任务的数据集,共包含8000个样本,格式为json文件。在每个json文件中有2个键,其中text对应藏文源语言新闻内容,summary对应中文目标语言新闻摘要。本数据集爬取自藏文新闻网站,为保证数据质量,在爬取数据时,去除了通讯社、图片、视频、图片、视频名称描述、报道记者等无关内容,只留下新闻的正文内容,然后借助现有的较成熟的藏汉翻译工具将藏文源语言新闻摘要翻译成中文目标语言摘要。同时为了进一步提高数据集的质量,本研究从摘要的事实一致性、充分性、流畅性等方面对数据集质量进行了评估,经筛选后得到了8000条质量较高的样本。本数据集的发布对推动藏汉跨语言摘要的发展具有重要价值。

关于医疗机构中药制剂的进展

中药制剂为传统医学的瑰宝,在现代医疗体系中占据重要地位。然而,随着科技的进步和医药标准的提高,中药制剂的研发与应用面临诸多挑战,如标准化生产、质量控制及与现代医学的融合等。通过深入探讨中药制剂的最新进展不仅能够更好地保护和传承中医药文化,还可以为其现代化和国际化贡献思路,同时为患者提供更多安全有效的治疗选择。所以本文综述了医疗机构中药制剂的进展情况。首先介绍了中药制剂的基本概念、分类和应用场景,然后概述了中药制剂在医疗机构当中的使用情况。本文还分析了中药制剂的研究情况,包括新药的研发现状和挑战、中药剂型的研究进展、质量控制的研究现状和问题以及中药制剂质量控制的措施,分析中药制剂的应用前景,希望所得结果能为有关研究提供参考。