GenDocSum + MCLR: Generic document summarization based on maximum coverage and less redundancy

With the rapid growth of information on the Internet and electronic government recently, automatic multi-document summarization has become an important task. Multi-document summarization is an optimization problem requiring simultaneous optimization of more than one objective function. In this study, when building summaries from multiple documents, we attempt to balance two objectives, content coverage and redundancy. Our goal is to investigate three fundamental aspects of the problem, i.e. designing an optimization model, solving the optimization problem and finding the solution to the best summary. We model multi-document summarization as a Quadratic Boolean Programing (QBP) problem where the objective function is a weighted combination of the content coverage and redundancy objectives. The objective function measures the possible summaries based on the identified salient sentences and overlap information between selected sentences. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The QBP problem has been solved by using a binary differential evolution algorithm. Evaluation of the model has been performed on the DUC2002, DUC2004 and DUC2006 data sets. We have evaluated our model automatically using ROUGE toolkit and reported the significance of our results through 95% confidence intervals. The experimental results show that the optimization-based approach for document summarization is truly a promising research direction.     

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.     

MCMR: Maximum coverage and minimum redundant text summarization model

In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover key sentences in the given document(s) and cover the main content of the original document(s). This model also guarantees that in the summary can not be multiple sentences that convey the same information. The proposed model is quite general and can also be used for single- and multi-document summarization. We implemented our model on multi-document summarization task. Experimental results on DUC2005 and DUC2007 datasets showed that our proposed approach outperforms the baseline systems.     

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.     

基于DSC的多文本自动摘要

多文本摘要的目标是对给定的查询和多篇文本（文本集）,创建一个简洁明了的摘要,要求该摘要能够表达这些文本的关键内容,同时和给定的查询相关。一个给定的文本集通常包含一些主题,而且每个主题由一类句子来表示,一个优秀的摘要应该要包含那些最重要的主题。如今大部分的方法是建立一个模型来计算句子得分,然后选择得分最高的部分句子来生成摘要。不同于这些方法,我们更加关注文本的主题而不是句子,把如何生成摘要的问题看成一个主题的发现,排序和表示的问题。我们首次引入dominant sets cluster（DSC）来发现主题,然后建立一个模型来对主题的重要性进行评估,最后兼顾代表性和无重复性来从各个主题中选择句子组成摘要。我们在DUC2005、2006、2007三年的标准数据集上进行了实验,最后的实验结果证明了该方法的有效性。     

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.     

Multi-document summarization via submodularity

Multi-document summarization is becoming an important issue in the Information Retrieval community. It aims to distill the most important information from a set of documents to generate a compressed summary. Given a set of documents as input, most of existing multi-document summarization approaches utilize different sentence selection techniques to extract a set of sentences from the document set as the summary. The submodularity hidden in the term coverage and the textual-unit similarity motivates us to incorporate this property into our solution to multi-document summarization tasks. In this paper, we propose a new principled and versatile framework for different multi-document summarization tasks using submodular functions (Nemhauser et al. in Math. Prog. 14(1):265?C294, 1978) based on the term coverage and the textual-unit similarity which can be efficiently optimized through the improved greedy algorithm. We show that four known summarization tasks, including generic, query-focused, update, and comparative summarization, can be modeled as different variations derived from the proposed framework. Experiments on benchmark summarization data sets (e.g., DUC04-06, TAC08, TDT2 corpora) are conducted to demonstrate the efficacy and effectiveness of our proposed framework for the general multi-document summarization tasks.     

A spectral analysis approach to document summarization: Clustering and ranking sentences simultaneously

Automatic document summarization aims to create a compressed summary that preserves the main content of the original documents. It is a well-recognized fact that a document set often covers a number of topic themes with each theme represented by a cluster of highly related sentences. More important, topic themes are not equally important. The sentences in an important theme cluster are generally deemed more salient than the sentences in a trivial theme cluster. Existing clustering-based summarization approaches integrate clustering and ranking in sequence, which unavoidably ignore the interaction between them. In this paper, we propose a novel approach developed based on the spectral analysis to simultaneously clustering and ranking of sentences. Experimental results on the DUC generic summarization datasets demonstrate the improvement of the proposed approach over the other existing clustering-based approaches.     

CDDS: Constraint-driven document summarization models

This paper proposes a constraint-driven document summarization approach emphasizing the following two requirements: (1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary and (2) sufficient coverage, which focuses on avoiding the loss of the document’s main information when generating the summary. The constraint-driven document summarization models with tuning the constraint parameters can drive content coverage and diversity in a summary. The models are formulated as a quadratic integer programming (QIP) problem. To solve the QIP problem we used a discrete PSO algorithm. The models are implemented on multi-document summarization task. The comparative results showed that the proposed models outperform other methods on DUC2005 and DUC2007 datasets.     

Multi-document summarization based on the Yago ontology

Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semantics-based models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process.This paper proposes a novel summarizer, namely Yago-based Summarizer, that relies on an ontology-based evaluation and selection of the document sentences. To capture the actual meaning and context of the document sentences and generate sound document summaries, an established entity recognition and disambiguation step based on the Yago ontology is integrated into the summarization process.The experimental results, which were achieved on the DUC’04 benchmark collections, demonstrate the effectiveness of the proposed approach compared to a large number of competitors as well as the qualitative soundness of the generated summaries.     

SumCR: A new subtopic-based extractive approach for text summarization

In text summarization, relevance and coverage are two main criteria that decide the quality of a summary. In this paper, we propose a new multi-document summarization approach SumCR via sentence extraction. A novel feature called Exemplar is introduced to help to simultaneously deal with these two concerns during sentence ranking. Unlike conventional ways where the relevance value of each sentence is calculated based on the whole collection of sentences, the Exemplar value of each sentence in SumCR is obtained within a subset of similar sentences. A fuzzy medoid-based clustering approach is used to produce sentence clusters or subsets where each of them corresponds to a subtopic of the related topic. Such kind of subtopic-based feature captures the relevance of each sentence within different subtopics and thus enhances the chance of SumCR to produce a summary with a wider coverage and less redundancy. Another feature we incorporate in SumCR is Position, i.e., the position of each sentence appeared in the corresponding document. The final score of each sentence is a combination of the subtopic-level feature Exemplar and the document-level feature Position. Experimental studies on DUC benchmark data show the good performance of SumCR and its potential in summarization tasks.     

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.     

A new sentence similarity measure and sentence based extractive technique for automatic text summarization

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. In our study we focus on sentence based extractive document summarization. We propose the generic document summarization method which is based on sentence clustering. The proposed approach is a continue sentence-clustering based extractive summarization methods, proposed in Alguliev [Alguliev, R. M., Aliguliyev, R. M., Bagirov, A. M. (2005). Global optimization in the summarization of text documents. Automatic Control and Computer Sciences 39, 42–47], Aliguliyev [Aliguliyev, R. M. (2006). A novel partitioning-based clustering method and generic document summarization. In Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI–IAT 2006 Workshops) (WI–IATW’06), 18–22 December (pp. 626–629) Hong Kong, China], Alguliev and Alyguliev [Alguliev, R. M., Alyguliev, R. M. (2007). Summarization of text-based documents with a determination of latent topical sections and information-rich sentences. Automatic Control and Computer Sciences 41, 132–140] Aliguliyev, [Aliguliyev, R. M. (2007). Automatic document summarization by sentence extraction. Journal of Computational Technologies 12, 5–15.]. The purpose of present paper to show, that summarization result not only depends on optimized function, and also depends on a similarity measure. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.     

利用维基百科实体增强基于图的多文档摘要

针对基于图的多文档摘要,该文提出了一种在图排序中结合维基百科实体信息增强摘要质量的方法。首先抽取文档集合中高频实体的维基词条内容作为该文档集合的背景知识,然后采用PageRank算法对文档集合中的句子进行排序,之后采用改进的DivRank算法对文档集合和背景知识中的句子一起排序,最后根据两次排序结果的线性组合确定文档句子的最终排序以进行摘要句的选取。在DUC2005数据集上的评测结果表明该方法可以有效利用维基百科知识增强摘要的质量。     

Extractive single-document summarization based on genetic operators and guided local search

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form – and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence – such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-SingleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.     

基于非平衡数据分类的单文档自动文摘方法

自动文摘是自然语言处理领域的一个重要研究话题,基于机器学习的自动文摘方法则是该项研究中的一个热点。然而,自动文摘问题中的数据分布有一个重要现象,即文摘句子与非文摘句子的数量相差非常悬殊,该现象将给传统机器学习算法的应用效果带来负面影响。为此,本文针对自动文摘中句子类别分布严重不平衡这一现象,以支持向量机算法为基础,设计了两种有效的处理非平衡自动文摘数据的分类方法。在第一种方法中,将传统支持向量机中正负类平衡的分类间隔转换为不平衡的分类间隔;在第二种方法中,通过将数据集进行切分,设计了一种支持向量机集成学习算法。通过在DUC2001数据集上的实验证明,本文设计的两种基于非平衡数据分类的单文档自动文摘方法显著优于基于传统分类算法的自动文摘方法。     

基于事件项语义图聚类的多文档摘要方法

基于事件的抽取式摘要方法一般首先抽取那些描述重要事件的句子,然后把它们重组并生成摘要。该文将事件定义为事件项以及与其关联的命名实体,并聚焦从外部语义资源获取的事件项语义关系。首先基于事件项语义关系创建事件项语义关系图并使用改进的DBSCAN算法对事件项进行聚类,接着为每类选择一个代表事件项或者选择一类事件项来表示文档集的主题,最后从文档抽取那些包含代表项并且最重要的句子生成摘要。该文的实验结果证明在多文档自动摘要中考虑事件项语义关系是必要的和可行的。     

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。     

结合LDA和谱聚类的多文档摘要

自动文摘技术的目标是致力于将冗长的文档内容压缩成较为简短的几段话,将信息全面、简洁地呈现给用户,提高用户获取信息的效率和准确率。所提出的方法在LDA（Latent Dirichlet Allocation）的基础上,使用Gibbs抽样估计主题在单词上的概率分布和句子在主题上的概率分布,结合LDA参数和谱聚类算法提取多文档摘要。该方法使用线性公式来整合句子权重,提取出字数为400字的多文档摘要。使用ROUGE自动摘要评测工具包对DUC2002数据集评测摘要质量,结果表明,该方法能有效地提高摘要的质量。     

SSC软聚类算法在面向查询的多文档文摘中的应用

针对面向查询的多文档自动文摘,本文将查询句混入多文档集合中的各句子中间,采用高效的软聚类算法SSC对所有的句子进行聚类。采用轮转法抽取文摘句,最后生成文摘。该方法在DUC2005的语料中测试效果很好。