主题与子事件发现的多文档自动文摘

提出了一种基于主题与子事件抽取的多文档自动文摘方法。该方法突破传统词频统计方法,除考虑词语频率、位置信息外,还将词语是否为描述文本集合的主题和子事件作为因素,提取出了8个基本特征,利用逻辑回归模型预测基本特征对词语权重的影响,计算词语权重。通过建立句子向量空间模型给句子打分,结合句子分数和冗余度产生文摘。对N-gram同现频率、主题词覆盖率和高频词覆盖率3种不同参数,分别在Coverage Baseline、Centroid-Based Summary和Word Mining based Summary（WMS）3种不同文摘系统下所产生的文摘质量,进行了对比实验,结果表明WMS系统在多方面具有优越的性能。     

GA, MR, FFNN, PNN and GMM based models for automatic text summarization

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First, we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. Moreover, we use all feature parameters to train feed forward neural network (FFNN), probabilistic neural network (PNN) and Gaussian mixture model (GMM) in order to construct a text summarizer for each model. Furthermore, we use trained models by one language to test summarization performance in the other language. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 Arabic political articles and 100 English religious articles. The results of the proposed approach are promising, especially the GMM approach.     

基于加权TextRank的中文自动文本摘要

现有中文自动文本摘要方法主要是利用文本自身信息,其缺陷是不能充分利用词语之间的语义相关等信息。鉴于此,提出了一种改进的中文文本摘要方法。此方法将外部语料库信息用词向量的形式融入到TextRank算法中,通过TextRank与word2vec的结合,把句子中每个词语映射到高维词库形成句向量。充分考虑了句子之间的相似度、关键词的覆盖率和句子与标题的相似度等因素,以此计算句子之间的影响权重,并选取排序最靠前的句子重新排序作为文本的摘要。实验结果表明,此方法在本文数据集中取得了较好的效果,自动提取中文摘要的效果比原方法好。     

案件要素句子关联图卷积的案件舆情摘要方法

案件舆情摘要是从涉及特定案件的新闻文本簇中,抽取能够概括其主题信息的几个句子作为摘要.案件舆情摘要可以看作特定领域的多文档摘要,与一般的摘要任务相比,可以通过一些贯穿于整个文本簇的案件要素来表征其主题信息.在文本簇中,由于句子与句子之间存在关联关系,案件要素与句子亦存在着不同程度的关联关系,这些关联关系对摘要句的抽取有着重要的作用.提出了基于案件要素句子关联图卷积的案件文本摘要方法,采用图的结构来对多文本簇进行建模,句子作为主节点,词和案件要素作为辅助节点来增强句子之间的关联关系,利用多种特征计算不同节点间的关联关系.然后,使用图卷积神经网络学习句子关联图,并对句子进行分类得到候选摘要句.最后,通过去重和排序得到案件舆情摘要.在收集到的案件舆情摘要数据集上进行实验,结果表明：提出的方法相比基准模型取得了更好的效果,引入要素及句子关联图对案件多文档摘要有很好的效果.     

Hybrid method for text summarization based on statistical and semantic treatment

Text summarization presents several challenges such as considering semantic relationships among words, dealing with redundancy and information diversity issues. Seeking to overcome these problems, we propose in this paper a new graph-based Arabic summarization system that combines statistical and semantic analysis. The proposed approach utilizes ontology hierarchical structure and relations to provide a more accurate similarity measurement between terms in order to improve the quality of the summary. The proposed method is based on a two-dimensional graph model that makes uses statistical and semantic similarities. The statistical similarity is based on the content overlap between two sentences, while the semantic similarity is computed using the semantic information extracted from a lexical database whose use enables our system to apply reasoning by measuring semantic distance between real human concepts. The weighted ranking algorithm PageRank is performed on the graph to produce significant score for all document sentences. The score of each sentence is performed by adding other statistical features. In addition, we address redundancy and information diversity issues by using an adapted version of Maximal Marginal Relevance method. Experimental results on EASC and our own datasets showed the effectiveness of our proposed approach over existing summarization systems.

     

基于综合的句子特征的文本自动摘要

采用了一种综合的文本自动摘要方法来抽取出涵盖范围广、冗余信息少、最能反映文本中心思想的文本摘要.该方法充分考虑文本中的词频、标题、句子位置、线索词、提示性短语、句子相似度等特征因素,构建了一个综合的特征加权函数,运用数学回归模型对语料进行训练,去除冗余句子信息,提取关键句生成摘要.实验评估表明了该方法的可行性、有效性以及在摘要质量方面的优越性.     

一种基于演化算法进行句子抽取的多文档自动摘要系统SBGA

SBGA系统将多文档自动摘要过程视为一个从源文档集中抽取句子的组合优化过程,并用演化算法来求得近似最优解。与基于聚类的句子抽取方法相比,基于演化算法进行句子抽取的方法是面向摘要整体的,因此能获得更好的近似最优摘要。演化算法的评价函数中考虑了衡量摘要的4个标准:长度符合用户要求、信息覆盖率高、更多地保留原文传递的重要信息、无冗余。另外,为了提高词频计算的精度, SBGA采用了一种改进的词频计算方法TFS,将加权后词的同义词频率加到了原词频中。在DUC2004测试数据集上的实验结果表明,基于演化算法进行句子抽取的方法有很好的性能,其ROUGE-1分值比DUC2004最优参赛系统仅低0.55%。改进的词频计算方法TFS对提高文档质量也起到了良好的作用。     

基于超图的文本摘要与关键词协同抽取研究

Text summarization and keyword extraction are two important research topics in Natural Language Processing (NLP), and they both generate concise information to describe the gist of text. Although these two tasks have similar objective, they are usually studied independently and their association is less considered. Based on the graph-based ranking methods, some collaborative extraction methods have been proposed, capturing the associations between sentences, between words and between the sentence and the word. Though they generate both text summary and keywords in an iterative reinforced framework, most existing models are limited to express various kinds of binary relations between sentences and words, ignoring a number of potential important high-order relationships among different text units. In this paper, we propose a new collaborative extraction method based on hypergraph. In this method, sentences are modeled as hyperedges and words are modeled as vertices to build a hypergraph, and then the summary and keywords are generated by taking advantage of higher order information from sentences and words under the unified hypergraph. Experiments on the Weibo-oriented Chinese news summarization task in NLPCC 2015 demonstrate that the proposed method is feasible and effective.
Key words hypergraph;document Summarization;keyword extraction;collaborative extraction


     

面向法律文书的分段式摘要模型

文本摘要是指对文本信息内容进行概括、提取主要内容进而形成摘要的过程。现有的文本摘要模型通常将内容选择和摘要生成独立分析,虽然能够有效提高句子压缩和融合的性能,但是在抽取过程中会丢失部分文本信息,导致准确率降低。基于预训练模型和Transformer结构的文档级句子编码器,提出一种结合内容抽取与摘要生成的分段式摘要模型。采用BERT模型对大量语料进行自监督学习,获得包含丰富语义信息的词表示。基于Transformer结构,通过全连接网络分类器将每个句子分成3类标签,抽取每句摘要对应的原文句子集合。利用指针生成器网络对原文句子集合进行压缩,将多个句子集合生成单句摘要,缩短输出序列和输入序列的长度。实验结果表明,相比直接生成摘要全文,该模型在生成句子上ROUGE-1、ROUGE-2和ROUGE-L的F1平均值提高了1.69个百分点,能够有效提高生成句子的准确率。     

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.     

文本摘要的建构渗透度特征模型

文本摘要旨在实现从海量的文本数据中快速准确地获取关键信息。为探索新颖的摘要句特征因素,该文将文句中的关键词嵌入知识网络进行建模,并将文句映射至知识网络进行表达,进而提出文句的关键词建构渗透度特征模型,在摘要句判别中引入文句中关键词组的宽度和深度的渗透特性。结合最大熵建模分类方法,针对领域语料库进行不同特征的影响系数建模,实现了监督学习下摘要句的有效分类和自动提取。文中实验结果良好,表明了新特征模型的有效性和在领域语料库中的稳定性,且特征计算方法简洁,具有良好的综合实用性。     

基于谱聚类的多文档摘要新方法

提出一种基于谱聚类的多文档摘要方法。在将文档中主题相关的句子进行聚类的基础上,同时考虑不同主题类别的重要性,综合句子位置、长度等因素以得到句子的重要性得分。根据重要性从高到低抽取满足字数要求的句子作为最终摘要。实验结果表明,该方法相较于传统摘要方法有更好的性能,能够有效地提高摘要的质量。     

SyMSS: A syntax-based measure for short-text semantic similarity

Sentence and short-text semantic similarity measures are becoming an important part of many natural language processing tasks, such as text summarization and conversational agents. This paper presents SyMSS, a new method for computing short-text and sentence semantic similarity. The method is based on the notion that the meaning of a sentence is made up of not only the meanings of its individual words, but also the structural way the words are combined. Thus, SyMSS captures and combines syntactic and semantic information to compute the semantic similarity of two sentences. Semantic information is obtained from a lexical database. Syntactic information is obtained through a deep parsing process that finds the phrases in each sentence. With this information, the proposed method measures the semantic similarity between concepts that play the same syntactic role. Psychological plausibility is added to the method by using previous findings about how humans weight different syntactic roles when computing semantic similarity. The results show that SyMSS outperforms state-of-the-art methods in terms of rank correlation with human intuition, thus proving the importance of syntactic information in sentence semantic similarity computation.     

Automatic text summarization using latent semantic analysis

In the paper, the most state-of-the-art methods of automatic text summarization, which build summaries in the form of generic extracts, are considered. The original text is represented in the form of a numerical matrix. Matrix columns correspond to text sentences, and each sentence is represented in the form of a vector in the term space. Further, latent semantic analysis is applied to the matrix obtained to construct sentences representation in the topic space. The dimensionality of the topic space is much less than the dimensionality of the initial term space. The choice of the most important sentences is carried out on the basis of sentences representation in the topic space. The number of important sentences is defined by the length of the demanded summary. This paper also presents a new generic text summarization method that uses nonnegative matrix factorization to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state-of-the-art methods on the DUC 2001 and DUC 2002 standard data sets.     

融合主题特征的文本自动摘要方法研究

针对传统图模型方法进行文本摘要时只考虑统计特征或浅层次语义特征,缺乏对深层次主题语义特征的挖掘与利用,提出了融合主题特征后多维度度量的文本自动摘要方法MDSR(multi-dimension summarization rank)。首先利用LDA主题模型对文本主题语义信息进行挖掘,定义了主题重要度以衡量主题特征对句子重要程度的影响;然后结合主题特征、统计特征和句间相似度,改进了图模型节点的概率转移矩阵的构建方式;最后根据句子节点权重进行摘要的抽取与度量。实验结果显示,当主题特征、统计特征及句间相似度权重比例达到3:4:3时,MDSR方法的ROUGE评测值达到最佳,ROUGE-1、ROUGE-2、ROUGE-SU4值分别达到53.35%、35.18%和33.86%,优于对比方法,表明了融入主题特征后的文本摘要方法有效提高了摘要抽取的准确性。     

TSPT：基于预训练的三阶段复合式文本摘要模型

抽取式方法从源文本中抽取句子,会造成信息冗余;生成式方法可以生成非源文词,会产生语法问题,自然性差。BERT作为一种双向Transformer模型,在自然语言理解任务上展现了优异的性能,但在文本生成任务的应用有待探索。针对以上问题,提出一种基于预训练的三阶段复合式文本摘要模型（TSPT）,结合抽取式方法和生成式方法,将源本文经过预训练产生的双向上下文信息词向量由sigmoid函数获取句子得分抽取关键句,在摘要生成阶段将关键句作为完形填空任务重写,生成最终摘要。实验结果表明,该模型在CNN/Daily Mail数据集中取得了良好效果。     

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。     

Abstractive Text Summarization based on Improved Semantic Graph Approach

The goal of abstractive summarization of multi-documents is to automatically produce a condensed version of the document text and maintain the significant information. Most of the graph-based extractive methods represent sentence as bag of words and utilize content similarity measure, which might fail to detect semantically equivalent redundant sentences. On other hand, graph based abstractive method depends on domain expert to build a semantic graph from manually created ontology, which requires time and effort. This work presents a semantic graph approach with improved ranking algorithm for abstractive summarization of multi-documents. The semantic graph is built from the source documents in a manner that the graph nodes denote the predicate argument structures (PASs)—the semantic structure of sentence, which is automatically identified by using semantic role labeling; while graph edges represent similarity weight, which is computed from PASs semantic similarity. In order to reflect the impact of both document and document set on PASs, the edge of semantic graph is further augmented with PAS-to-document and PAS-to-document set relationships. The important graph nodes (PASs) are ranked using the improved graph ranking algorithm. The redundant PASs are reduced by using maximal marginal relevance for re-ranking the PASs and finally summary sentences are generated from the top ranked PASs using language generation. Experiment of this research is accomplished using DUC-2002, a standard dataset for document summarization. Experimental findings signify that the proposed approach shows superior performance than other summarization approaches.     

多文档文摘中句子优化选择方法研究

在多文档文摘子主题划分的基础上,提出了一种在子主题之间对文摘句优化选择的方法．首先在句子相似度计算的基础上,形成多文档集合的子主题,通过对各子主题打分,确定子主题的抽取顺序．以文摘中有效词的覆盖率作为优化指标,在各个子主题中选择文摘句．从减少子主题之间及子主题内部的信息的冗余性两个角度选择文摘句,使文摘的信息覆盖率得到很大提高．实验表明,生成的文摘是令人满意的．     

Automatic text summarization based on latent semantic indexing

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.