基于词项—句子—文档三层图模型的多文档自动摘要

应用图模型来研究多文档自动摘要是当前研究的一个热点,它以句子为顶点,以句子之间相似度为边的权重构造无向图结构。由于此模型没有充分考虑句子中的词项权重信息以及句子所属的文档信息,针对这个问题,该文提出了一种基于词项—句子—文档的三层图模型,该模型可充分利用句子中的词项权重信息以及句子所属的文档信息来计算句子相似度。在DUC2003和DUC2004数据集上的实验结果表明,基于词项—句子—文档三层图模型的方法优于LexRank模型和文档敏感图模型。     

一种基于语义与句法结构的短文本相似度计算方法

为了提高短文本语义相似度计算的准确率,提出一种新的计算方法:将文本分割为句子单元,对句子进行句法依存分析,句子之间相似度计算建立在词语间相似度计算的基础上,在计算词语语义相似度时考虑词语的新特征——情感特征,并提出一种综合方法对词语进行词义消歧,综合词的词性与词语所处的语境,再依据Hownet语义词典计算词语语义相似度;将句子中词语之间的语义相似度根据句子结构加权平均得到句子的语义相似度,最后通过一种新的方法——二元集合法——计算短文本的语义相似度。词语相似度与短文本相似度的准确率分别达到了87.63%和93.77%。实验结果表明,本文方法确实提高了短文本语义相似度的准确率。     

句子语义相似度计算

句子或文本片段相似度计算在与Web相关的任务中起着越来越重要的作用。在基于概念之间的语义相似度基础之上,提出一种句子语义相似度的计算方法SSBS并进行了相关的实验。与其他方法相比,SSBS方法在特征的量化过程中不仅考虑两个句子的概念对之间的语义相似度和字符串编辑距离,还考虑了不同词性的概念对句子相似度的影响。     

基于框架语义分析的汉语句子相似度计算

句子相似度计算在自然语言处理的许多领域中发挥着重要作用.已有的汉语句子相似度计算方法由于考虑句子的语义不全面,使得相似度计算结果不够准确,为此提出一种新的汉语句子相似度计算方法.该方法基于汉语框架网语义资源,通过多框架语义分析、框架的重要度度量、框架的相似匹配、框架间相似度计算等关键步骤来实现句子语义的相似度量.其中多框架语义分析是从框架角度对句子中的所有目标词进行识别、框架选择及框架元素标注,从而达到全面刻画句子语义的目的;在此基础上根据句子中框架的语义覆盖范围对不同框架的重要度进行区分,能够使得相似度结果更准确.在包含多目标词的句子集上的实验结果显示,基于多框架语义分析的句子相似度计算方法相对传统方法获得了更好的测试结果.     

融合词向量的多特征句子相似度计算方法研究

在归纳常见的句子相似度计算方法后,基于《人民日报》3.4万余份文本训练了用于语义相似度计算的词向量模型,并设计了一种融合词向量的多特征句子相似度计算方法。该方法在词方面,考虑了句子中重叠的词数和词的连续性,并运用词向量模型测量了非重叠词间的相似性;在结构方面,考虑了句子中重叠词的语序和两个句子的长度一致性。实验部分设计实现了4种句子相似度计算方法,并开发了相应的实验系统。结果表明:提出的算法能够取得相对较好的实验结果,对句子中词的语义特征和句子结构特征进行组合处理和优化,能够提升句子相似度计算的准确性。     

多特征融合的语句相似度计算模型

句子的相似度计算在自然语言处理的各个领域都占有十分重要的地位。提出了一种多特征融合的句子相似度计算模型,该计算方法把句子的词形、词序、结构、长度、距离和语义这6种特征相似度考虑进来,通过对不同的特征赋予不同的权重来调节各个特征对于句子相似度的贡献,从而使计算结果得到最优。实验结果表明,该方法与其他方法相比,描述句子的信息更加全面,在计算句子相似度方面具有较高的准确率。     

基于多特征的汉语句子相似度计算模型的研究

句子相似度的计算在自然语言处理的各个领域中都占有很重要的地位。文中深入分析了现有的一些句子相似度计算的方法,这些方法各自从词特征、词义特征或句法特征等某一侧面描述了句子相似的情况,未能全面地描述一个句子的完整信息。文中提出了一种新的基于多特征的汉语句子相似度的计算模型。该方法在基于词的基础上,从句子中词的表层到词的逻辑联系,从句子的局部结构到整体结构,用句子的区分度、相同词的相似度、长度相似度、词性相似度及词序相似度五个方面来综合考虑两个句子相似度的计算。实验结果表明,该方法合理、简便、可行。     

基于关系向量模型的句子相似度计算

句子相似度的计算在自然语言处理的各个领域占有很重要的地位,一些传统的计算方法只考虑句子的词形、句长、词序等表面信息,并没有考虑句子更深层次的语义信息,另一些考虑句子语义的方法在实用性上的表现不太理想。在空间向量模型的基础上提出了一种同时考虑句子结构和语义信息的关系向量模型,这种模型考虑了组成句子的关键词之间的搭配关系和关键词的同义信息,这些信息反应了句子的局部结构成分以及各局部之间的关联关系,因此更能体现句子的结构和语义信息。以关系向量模型为核心,提出了基于关系向量模型的句子相似度计算方法。同时将该算法应用到网络热点新闻自动摘要生成算法中,排除文摘中意思相近的句子从而避免文摘的冗余。实验结果表明,在考虑网络新闻中的句子相似度时,与考虑词序与语义的算法相比,关系向量模型算法不但提高了句子相似度计算的准确率,计算的时间复杂度也得到了降低。     

基于语义角色标注的汉语句子相似度算法

在语义角色标注过程中,经常需要检索相似的已标注语料,以便进行参考和分析。现有方法未能充分利用动词及其支配的成分信息,无法满足语义角色标注的相似句检索需求。基于此,本文提出一种新的汉语句子相似度计算方法。该方法基于已标注好语义角色的语料资源,以动词为分析核心,通过语义角色分析、标注句型的相似匹配、标注句型间相似度计算等步骤来实现句子语义的相似度量。为达到更好的实验效果,论文还综合比较了基于知网、词向量等多种计算词语相似度的算法,通过分析与实验对比,将实验效果最好的算法应用到句子相似度计算的研究中。实验结果显示,基于语义角色标注的句子相似度计算方法相对传统方法获得了更好的测试结果。     

一种文本分割技术的多文档文摘方法研究

提出一种基于文本分割技术的多文档自动文摘方法。该方法使用HowNet作为概念获取工具,通过建立句子概念向量空间模型和利用改进的DotPlotting模型来进行文本分割。利用建立的句子概念向量空间模型计算句子重要度,并根据句子重要度、文本分割结果和文摘句相似度等因素产生文本摘要。使用ROUGE-N评测方法和F_Score作为评测指标对系统产生的文摘进行评测,结果显示使用文本分割技术进行多文档摘要是有效的。     

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.     

Multiple documents summarization based on evolutionary optimization algorithm

This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization.     

iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network

Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops an approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

CLUSTERING TECHNIQUES AND DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR MULTI‐DOCUMENT SUMMARIZATION

Multi‐document summarization is a process of automatic creation of a compressed version of a given collection of documents that provides useful information to users. In this article we propose a generic multi‐document summarization method based on sentence clustering. We introduce five clustering methods, which optimize various aspects of intra‐cluster similarity, inter‐cluster dissimilarity and their combinations. To solve the clustering problem a modification of discrete particle swarm optimization algorithm has been proposed. The experimental results on open benchmark data sets from DUC2005 and DUC2007 show that our method significantly outperforms the baseline methods for multi‐document summarization.     

The combination of term relations analysis and weighted frequent itemset model for multidocument summarization

Nowadays, it is necessary that users have access to information in a concise form without losing any critical information. Document summarization is an automatic process of generating a short form from a document. In itemset-based document summarization, the weights of all terms are considered the same. In this paper, a new approach is proposed for multidocument summarization based on weighted patterns and term association measures. In the present study, the weights of the terms are not equal in the context and are computed based on weighted frequent itemset mining. Indeed, the proposed method enriches frequent itemset mining by weighting the terms in the corpus. In addition, the relationships among the terms in the corpus have been considered using term association measures. Also, the statistical features such as sentence length and sentence position have been modified and matched to generate a summary based on the greedy method. Based on the results of the DUC 2002 and DUC 2004 datasets obtained by the ROUGE toolkit, the proposed approach can outperform the state-of-the-art approaches significantly.     

The two-stage unsupervised approach to multidocument summarization

This paper suggests an approach for creating a summary for a set of documents with revealing the topics and extracting informative sentences. The topics are determined through clustering of sentences, and the informative sentences are extracted using the ranking algorithm. The result of the summarization has been shown depends on the clustering method, the ranking algorithm, and the similarity measure. The experiments on an open benchmark datasets DUC2001 and DUC2002 have showed that the suggested clustering methods and the ranking algorithm show better results than the known k-means method and the ranking algorithms PageRank and HITS.     

多文档文本摘要的一种改进HITS算法

提出了一种对HITS算法进行改进的新方法，本方法将文档内容与一些启发信息如“短语”，“句子长度”和“首句优先”等结合，用于发现多文档子主题，并且将文档子主题特征转换成图节点进行排序。通过对DUC2004数据的实验，结果显示本方法是一种有效的多文本摘要方法。     

Formulation of document summarization as a 0–1 nonlinear programming problem

We proposed a novel text summarization model based on 0–1 non-linear programming problem. This proposed model covers the main content of the given document(s) through sentence assignment. We implemented our model on multi-document summarization task. When comparing our method to several existing summarization methods on an open DUC2001 and DUC2002 datasets, we found that the proposed method could improve the summarization results significantly. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-W metrics.