AN OPTIMIZATION APPROACH TO AUTOMATIC GENERIC DOCUMENT SUMMARIZATION

In this paper, we have presented an optimization approach to document summarization. The potential of optimization based document summarization models has not been well explored to date. This is partially the difficulty to formulate the criteria used for objective assessment. We modeled document summarization as the linear and nonlinear optimization problems. These models generally attempt simultaneously to balance coverage and diversity in the summary. To solve the optimization problem we developed a novel particle swarm optimization (PSO) algorithm. Experiments showed our linear and nonlinear models produce very competitive results, which significantly outperform the NIST baselines in both years. More important, although linear and nonlinear models are comparable to the top three systems S24, S15, and S12 in the DUC2006, they are even superior to the best participating system in the DUC2005.     

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.     

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.     

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.     

Extractive multi-document summarization using population-based multicriteria optimization

Multi-document summarization is the process of extracting salient information from a set of source texts and present that information to the user in a condensed form. In this paper, we propose a multi-document summarization system which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun, Noun, Verb and Adjective set. Five features, such as TF_ISF, Aggregate Cross Sentence Similarity, Title Similarity, Proper Noun and Sentence Length associated with the sentences, are extracted, and scores are assigned to sentences based on these features. Weights that can be assigned to different features may vary depending upon the nature of the document, and it is hard to discover the most appropriate weight for each feature, and this makes generation of a good summary a very tough task without human intelligence. Multi-document summarization problem is having large number of decision parameters and number of possible solutions from which most optimal summary is to be generated. Summary generated may not guarantee the essential quality and may be far from the ideal human generated summary. To address this issue, we propose a population-based multicriteria optimization method with multiple objective functions. Three objective functions are selected to determine an optimal summary, with maximum relevance, diversity, and novelty, from a global population of summaries by considering both the statistical and semantic aspects of the documents. Semantic aspects are considered by Latent Semantic Analysis (LSA) and Non Negative Matrix Factorization (NMF) techniques. Experiments have been performed on DUC 2002, DUC 2004 and DUC 2006 datasets using ROUGE tool kit. Experimental results show that our system outperforms the state of the art works in terms of Recall and Precision.     

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.     

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.     

COSUM: Text summarization based on clustering and optimization

Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures.     

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。     

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.     

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.     

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。     

A New Approach for Multi-Document Update Summarization

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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.     

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。     

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.     

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.     

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。     

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.