Multi-document Summarization System Research Articles

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures

Because of the massive amount of textual information accessible today, automated extraction text summarization is one of the most extensively used ways to organize the information. The summarization mechanisms help to extract the important topics of data from a given set of documents. Extractive summarization is one method for providing a representative summary of a text by choosing the most pertinent sentences from the original text. Extractive multi-document text summarization systems' primary goal is to decrease the quantity of textual information in a document collection by concentrating on the most crucial subjects and removing irrelevant material. In the previous research, there are several methods such as term-weighting schemes and similarity metrics used for constructing an automated summary system. There are few studies that look at the performance of combining various Semantic similarity and word weighting techniques in automatic text summarization. We evaluated numerous semantic similarity metrics in extractive multi-document text summarization in this research. In the extractive multi-document text summarization discussed in this research, we looked at numerous semantic similarity metrics. ROUGE metrics have been used to evaluate the model performance in experiments using DUC datasets. Even more, the combination formed by different semantic similarity measures obtained the highest results in comparison with the other models.

A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA

The explosive growth of textual data on the web and the problem of obtaining desired information through this enormous volume of data has led to a dramatic increase in demand for developing automatic text summarization systems. For this reason, this paper presents a novel multi-document text summarization approach, called MTSQIGA, which extracts salient sentences from source document collection to generate the summary. The proposed generic summarizer models extractive summarization as a binary optimization problem that applies a modified quantum-inspired genetic algorithm (QIGA) in its processing stage to find the best solution. Objective function of our approach plays an important role in optimizing linear combination of coverage, relevance, and redundancy factors which consists of six sentence scoring measures. To ensures the generation of a summary with predefined length limit, the presented QIGA employs a modified quantum measurement and a self-adaptive quantum rotation gate based on the quality and length of the summary. Evaluation of the proposed system was performed on DUC 2005 and 2007 benchmark datasets in terms of ROUGE standard measures. Comparison of MTSQIGA with existing state-of-the-art approaches for multi-document summarization shows superior performance of the proposed systems over other methods on both existing benchmark datasets. It also indicates promising efficiency of our proposed algorithm on applying quantum-inspired genetic algorithm to the text summarization tasks.

ViMs: a high-quality Vietnamese dataset for abstractive multi-document summarization

Automatic text summarization is important in this era due to the exponential growth of documents available on the Internet. In the Vietnamese language, VietnameseMDS is the only publicly available dataset for this task. Although the dataset has 199 clusters, there are only three documents in each cluster, which is small compared to typical datasets in English. This motivates us to construct ViMs—a big and high-quality Vietnamese dataset for abstractive multi-document summarization. To that end, we recruited 29 annotators and enhanced MDSWriter—an open-source annotation tool, to support the annotators in creating gold standard summaries. As a result, ViMs has 600 summaries corresponding to 300 clusters of 1,945 documents. We have verified the reliability of our dataset by using a variety of metrics including conventional Cohen’s $$\kappa $$ , relaxed Cohen’s $$\kappa $$ —a new metric that we propose to make it more suitable for abstractive summarization, and ROUGE scores. A relaxed $$\kappa $$ score of 0.55 indicate that ViMs could attain moderate agreement between annotators. Meanwhile, ROUGE scores are 0.729 of ROUGE-1, 0.507 of ROUGE-2 and 0.524 of ROUGE-SU4. We have further evaluated ViMs by using three different summarization systems: TextRank, CFVi and MUSEEC. Their performances are 0.628, 0.711 and 0.732 of ROUGE-1, respectively. These results show that the ViMs dataset is suitable for both training and evaluating multi-document summarization systems. We have made the dataset and evaluation results of this work publicly available for research community. It is noted that unlike previous work that only published the final summarization dataset, we also publish intermediate annotation results, which can be used in other NLP problems such as sentence classification.

Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

The increasing usage of the Internet and social networks has produced a significant amount of online textual data. These online textual data led to information overload and redundancy. It is important to eliminate the information redundancy and preserve the time required for reading these online textual data. Thus, there is a persistent need for an automatic text summarization system, which extract the relevant and salient information from a collection of documents, that sharing the same or related topics. Then, presenting this extracted information in a condensed form to preserve the main topics. This paper proposes an automatic, generic, and extractive Arabic multi-document summarization system. The proposed system employs the clustering-based and evolutionary multi-objective optimization methods. The clustering-based method discovers the main topics in the text, while the evolutionary multi-objective optimization method optimizes three objectives based on coverage, diversity/redundancy, and relevancy. The performance of the proposed system is evaluated using TAC 2011 and DUC 2002 datasets. The experimental results are compared using ROUGE evaluation measure. The obtained results showed the effectiveness of the proposed system compared to other peer systems. The proposed system outperformed other peer systems for all ROUGE metrics using TAC 2011. We achieved an F-measure of 38.9%, 17.7%, 35.4%, and 15.8% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively. In addition, the proposed system with DUC 2002 dataset achieved an F-measure of 47.1%, 23.7%, 47.1%, 20.4% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively.

An ontology-based information extraction and summarization of multiple news articles

Ontology based information extraction and summarization process in news content retrieved the news based on the user query. The user query should be of any context about the news content. So that, users need not be aware of the information that they search. This type of ontology based multi-document summarization system mainly focused on abstract document found in the news content. So, we proposed a work to combine information extraction, Abstraction based summarization and natural language generation to generate efficient multi-document summarization from multiple news articles. In this paper, we proposed an IE-supported summarization system that automatically extracts keyword for text summarization in Tamil e-newspaper datasets. The system first requires that the user need to select the particular domain. A set of news articles related to the domain is searched by search engine. One or more scenario templates (likes extraction domains such as Science, and technology, sports and natural disasters) are used to activate the system. The user optionally provides filters and preferences on the scenario template slots, specifying what information he/she wants to be reported in the summary. Then the system invokes the Summarizer to generate a natural language summary of the extracted information subject to the user’s constraints.

Neural sentence fusion for diversity driven abstractive multi-document summarization

The lack of multi-document based models and the inaccuracy in representing multiple long documents into a fixed size vector inspired us to solve abstractive multi-document summarization. Also, there is lack of good multi-document based human-authored datasets to train any encoder-decoder models. To overcome this, we have designed complementary models for two different tasks such as sentence clustering and neural sentence fusion. In this work, we minimize the risk of producing incorrect fact by encoding a related set of sentences as an input to the encoder. We have applied our complementary models to implement a full abstractive multi-document summarization system which simultaneously considers importance, coverage, and diversity under a desired length limit. We conduct extensive experiments for all the proposed models which bring significant improvements over the state-of-the-art methods across different evaluation metrics.

Extractive multi-document summarization based on textual entailment and sentence compression via knapsack problem

AbstractBy increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.

Extractive multi-document summarization using population-based multicriteria optimization

Abstract 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.

An Adaptive Semantic Descriptive Model for Multi-Document Representation to Enhance Generic Summarization

Due to the increasing accessibility of online data and the availability of thousands of documents on the Internet, it becomes very difficult for a human to review and analyze each document manually. The sheer size of such documents and data presents a significant challenge for users. Providing automatic summaries of specific topics helps the users to overcome this problem. Most of the current extractive multi-document summarization systems can successfully extract summary sentences; however, many limitations exist which include the degree of redundancy, inaccurate extraction of important sentences, low coverage and poor coherence among the selected sentences. This paper introduces an adaptive extractive multi-document generic (EMDG) methodology for automatic text summarization. The framework of this methodology relies on a novel approach for sentence similarity measure, a discriminative sentence selection method for sentence scoring and a reordering technique for the extracted sentences after removing the redundant ones. Extensive experiments are done on the summarization benchmark datasets DUC2005, DUC2006 and DUC2007. This proves that the proposed EMDG methodology is more effective than the current extractive multi-document summarization systems. Rouge evaluation for automatic summarization is used to validate the proposed EMDG methodology, and the experimental results showed that it is more effective and outperforms the baseline techniques, where the generated summary is characterized by high coverage and cohesion.

Event-based Multi-document Summarization

Daily amount of news reporting real-world events is growing exponentially. At the same time, Organizations are looking for information about current and past events that affects them, such as mergers and acquisitions of companies. The Organizations need to obtain event information in a fast and summarized form to make decisions. Event-based retrieval and summarization systems offer an efficient solution to this problem. Most summarization research work uses news stories. Although this type of documents is characterized by conveying information about events, almost all work concentrates on approaches that do not take into account this aspect. The proposed multi-document summarization methods are based on the hierarchical combination of single-document summaries. We improved our multi-document summarization methods using event information. Our approach is based on a two-stage single-document\ method that extracts a collection of key phrases, which are then used in a centrality-asrelevance passage retrieval model. To adapt centrality-as-relevance single-document summarization for multi-document summarization that is able to use event information, we needed a good and adaptable baseline system. Because the key phrase extraction play a significant role in the summarization, we improved a state-of-the-art key phrase extraction toolkit using four additional sets of semantic features. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. We explored three different ways to integrate event information, achieving state-of-the-art results in both single and multi-document summarization using filtering and event-based features. To cope with the possible usage of different terms to describe the same event, we explored distributed representations of text in the form of word embeddings, which contributed to improve the multi-document summarization results. The automatic evaluation and user study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE- 1 scores of 16% for TAC 2009 and of 17% for DUC 2007. We have also obtained improvements in ROUGE-1 upon current state-of-the-art single-document summarization systems of between 32% in clean data and 19% in noisy data. These improvements derived from the inclusion of key phrases and event information. The extraction of key phrases was also refined with additional pre-processing steps and features, which lead to a relative improvement in NDCG scores of 9%. The introduction of Fuzzy Fingerprints for event detection enabled the detection of all event types, while the best competitor, an SVM with enhanced features, only detects roughly 85% of the different types of events. This lead to a large increase in the G-Mean and variants results when using the Fuzzy Fingerprints method. This doctoral work was carried under the Dual Ph.D. program in Languages and Information Technologies of Carnegie Mellon Portugal Program. The program took place at both the Language Technologies Institute at Carnegie Mellon University (CMU) and at the Department of Computer of Science and Engineering at Instituto Superior Técnico (IST). The doctoral work was performed under the supervision of University Professor Jaime Carbonell (CMU), Distinguished Career Professor Anatole Gershman (CMU), Assistant Professor David Martins de Matos (IST), and Assistant Professor Joo P. Neto (IST). Full Professor Ricardo Baeza-Yates (Yahoo!Research/Univ. Pompeu Fabra/Univ. de Chile), Research Professor Eduard Hovy (CMU), Associate Professor Ana Paiva (IST), and Full Professor Isabel Trancoso served as dissertation committee members.

An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy.

Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and F-score than the existing clustering with lexical chaining approach.

Exploring events and distributed representations of text in multi-document summarization

In this article, we explore an event detection framework to improve multi-document summarization. Our approach is based on a two-stage single-document method that extracts a collection of key phrases, which are then used in a centrality-as-relevance passage retrieval model. We explore how to adapt this single-document method for multi-document summarization methods that are able to use event information. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. To cope with the possible usage of different terms to describe the same event, we explore distributed representations of text in the form of word embeddings, which contributed to improve the summarization results. The proposed summarization methods are based on the hierarchical combination of single-document summaries. The automatic evaluation and human study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE-1 scores of 16% for TAC 2009 and of 17% for DUC 2007.

Deep Dependency Substructure-Based Learning for Multidocument Summarization

Most extractive style topic-focused multidocument summarization systems generate a summary by ranking textual units in multiple documents and extracting a proper subset of sentences biased to the given topic. Usually, the textual units are simply represented as sentences or n-grams, which do not carry deep syntactic and semantic information. This article presents a novel extractive topic-focused multidocument summarization framework. The framework proposes a new kind of more meaningful and informative units named frequent Deep Dependency Sub-Structure (DDSS) and a topic-sensitive Multi-Task Learning (MTL) model for frequent DDSS ranking. Given a document set, first, we parse all the sentences into deep dependency structures with a Head-driven Phrase Structure Grammar (HPSG) parser and mine the frequent DDSSs after semantic normalization. Then we employ a topic-sensitive MTL model to learn the importance of these frequent DDSSs. Finally, we exploit an Integer Linear Programming (ILP) formulation and use the frequent DDSSs as the essentials for summary extraction. Experimental results on two DUC datasets demonstrate that our proposed approach can achieve state-of-the-art performance. Both the DDSS information and the topic-sensitive MTL model are validated to be very helpful for topic-focused multidocument summarization.

Density-based graph model summarization: Attaining better performance and efficiency

Several algorithms based on PageRank algorithm have been proposed to rank the document sentences in the multidocument summarization field and LexRank and T-LexRank algorithms are well known examples. In literature different concepts such as weighted inter-cluster edge, cluster-sensitive graph model and document-sensitive graph model have been proposed to improve LexRank and T-LexRank algorithms (e.g. DsR-G, DsR-Q) for multi-document summarization. In this paper, a density-based graph model for multi-document summarization is proposed by adding the concept of density to LexRank and T-LexRank algorithms. The resulting generic multi-document summarization systems, DensGS and DensGSD were evaluated on DUC 2004 while the query-based variants, DensQS, DensQSD were evaluated on DUC 2006, DUC 2007 and TAC 2010 task A. ROUGE measure was used in the evaluation. Experimental results show that density concept improves LexRank and T-LexRank algorithms and outperforms previous graph-based models (DsR-G and DsR-Q) in generic and query-based multi-document summarization tasks. Furthermore, the comparison of the number of iterations indicates that the density-based algorithm is faster than the other algorithms based on PageRank.

Automatic Naming of Domain Specific Clusters for Efficient Searching

This paper proposes a new and efficient methodology for clustering of html documents. The topic wise categorization of documents into different clusters makes searching easier and efficient. This technique can be utilized by search engines to provide relevant results to the user according to query and also utilized by online journal domains that are maintaining large set of documents. This paper suggests a good word matching and naming of automatic generated clusters , so, the time consume for finding the appropriate cluster for a document will be reduced. This paper shows the use of an efficient technique for finding the similarity between the documents and assigns them a proper cluster. The proper clustering of documents will be further utilized by multidocument summarization system, which produces a summary for the documents related to each other.

Vertex Cover Algorithm Based Multi-document Summarization Using Information Content of Sentences

In recent times, the requirement for the generation of multi-document summary has gained a lot of attention among researchers. Multi-document summarization systems focus on generating compressed form of the documents which maintains the pertinent features of the original documents. Mostly, text summarization techniques use the sentence extraction technique where the salient sentences in the multiple documents are selected and presented as a summary. In our proposed system, we have developed a sentence extraction based multi-document summarization system using the principle of vertex cover algorithm which automatically selects relevant sentences that cover the predominant concepts of the input documents. This frame work represents the documents as a weighted undirected graph with sentences as the vertices and the similarity between the sentences as the edge weight between the corresponding vertices in the graph. The experimental result on the DUC 2002 data sets demonstrates the effectiveness of the proposed method in document summarization

SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization

Extractive multi-document summarization systems usually rank sentences in a document set with some ranking strategy and then select a few highly ranked sentences into the summary. One of the most popular ranking algorithms is the graph-based ranking algorithm. In this paper, we investigate making use of semantic role information to enhance the graph-based ranking algorithm for multi-document summarization. We first parse the sentences and obtain the semantic roles, and then propose a novel SRRank algorithm and two extensions to make better use of the semantic role information. Our proposed algorithms can simultaneously rank the sentences, semantic roles and words in a heterogeneous ranking process. Experimental results on two DUC datasets demonstrate that our proposed algorithms significantly outperform a few baselines, and the semantic role information is validated to be very helpful for multi-document summarization.

Trends in Multi-document Summarization System Methods

formation is knowledge if it is rightly applied. Information are stored with different formats in databases but retrieving such from different documents has been a challenge. People want ready-made information for the purpose of decision making in minimal time and thereby crave for summary of information. Automatic summarization helps in mining data and delivering timely and cogent information to users. These systems attempt to address the issue of data mining using different summarization methods. This paper discusses existing methods and state of the art in automatic summarisation system from recent articles. Achievement and challenges involve are also discussed.

FoDoSu: Multi-document summarization exploiting semantic analysis based on social Folksonomy

Multi-document summarization techniques aim to reduce documents into a small set of words or paragraphs that convey the main meaning of the original document. Many approaches to multi-document summarization have used probability-based methods and machine learning techniques to simultaneously summarize multiple documents sharing a common topic. However, these techniques fail to semantically analyze proper nouns and newly-coined words because most depend on an out-of-date dictionary or thesaurus. To overcome these drawbacks, we propose a novel multi-document summarization system called FoDoSu, or Folksonomy-based Multi-Document Summarization, that employs the tag clusters used by Flickr, a Folksonomy system, for detecting key sentences from multiple documents. We first create a word frequency table for analyzing the semantics and contributions of words using the HITS algorithm. Then, by exploiting tag clusters, we analyze the semantic relationships between words in the word frequency table. Finally, we create a summary of multiple documents by analyzing the importance of each word and its semantic relatedness to others. Experimental results from the TAC 2008 and 2009 data sets demonstrate the improvement of our proposed framework over existing summarization systems.