Multi-document Summarization Research Articles

Knowledge Base Driven Automatic Text Summarization using Multi-objective Optimization

Automatic Text summarization aims to automati-cally generate condensed summary from a large set of documents on the same topic. We formulate text summarization task as a multi-objective optimization problem by defining information coverage and diversity as two conflicting objective functions. With this formulation, we propose a novel technique to improve the performance using a knowledge base. The main rationale of the approach is to extract important text features of the original text by detecting important entities in a knowledge base. Next, an improvement on the multi-objective optimization algorithm is also proposed for the automatic text summarization problem. The focus is on improving efficiency of the each steps in the evolutionary multi-objective optimization process which is applicable to all tasks with the same problem formulation. The result summary of the suggested method ensure the maximum coverage of the original documents and the diversity of the sentences in the summary among each other. The experiments on DUC2002 and DUC2004 multi-document summarization task dataset shows that the proposed model is effective compared to other methods.

The impact of term-weighting schemes and similarity measures on extractive multi-document text summarization

Automatic text summarization is currently a topic of great interest in many knowledge fields. Extractive multi-document text summarization methods aim to reduce the textual information from a document collection by covering the main content and reducing the redundant information. In the scientific literature, there are different approaches related to term-weighting schemes and similarity measures, which are necessary for implementing an automatic summary system. However, to the best of the authors’ knowledge, there are no studies to analyze the performance of the different schemes and measures. In this paper, all possible combinations of the most common term-weighting schemes and similarity measures used in the extractive multi-document text summarization field have been implemented, compared, and analyzed. Experiments have been performed with Document Understanding Conferences (DUC) datasets, and the model performance has been assessed with eight Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the execution time. Results show that the best term-weighting scheme is the term-frequency inverse-sentence-frequency scheme, and the best similarity measure is the cosine similarity. Even more, the combination formed by both of them has obtained the best average results in 87.5% of ROUGE scores compared to the other combinations.

A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion

Opinion summarization is a process to produce concise summaries from a large number of opinionated texts. In this paper, we present a novel deep-learning-based method for the generic opinion-oriented extractive summarization of multi-documents (also known as RDLS). The method comprises sentiment analysis embedding space (SAS), text summarization embedding spaces (TSS) and opinion summarizer module (OSM). SAS employs recurrent neural network (RNN) which is composed by long short-term memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about a word have vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the existing drawbacks. TSS exploits multiple sources of statistical and linguistic knowledge features to augment word-level embedding and extract a proper set of sentences from multiple documents. TSS also uses the Restricted Boltzmann Machine algorithm to enhance and optimize those features and improve resultant accuracy without losing any important information. OSM consists of two phases: sentence classification and sentence selection which work together to produce a useful summary. Experiment results show that RDLS outperforms other existing methods. Moreover, the ensemble of statistical and linguistic knowledge, sentiment knowledge, sentiment shifter rules and word-embedding model allows RLDS to achieve significant accuracy.

Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization

For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.

A deep neural architecture based meta-review generation and final decision prediction of a scholarly article

Peer reviews form an essential part of scientific communications. Research papers and proposals are reviewed by several peers before they are finally accepted or rejected for publication and funding, respectively. With the steady increase in the number of research domains, scholarly venues (journal and/or conference), researchers, and papers, managing the peer review process is becoming a daunting task. Application of recommender systems to assist peer reviewing is, therefore, being explored and becoming an emerging research area. In this paper, we present a deep learning network based Meta-Review Generation considering peer review prediction of the scholarly article (MRGen). MRGen is able to provide solutions for: (i) Peer review prediction (Task 1) and (ii) Meta-review generation (Task 2). First, the system takes the peer reviews as input and produces a draft meta-review. Then it employs an integrated framework of convolution layer, long short-term memory (LSTM) model, Bi-LSTM model, and attention mechanism to predict the final decision (accept/reject) of the scholarly article. Based on the final decision, the proposed model MRGen incorporates Pointer Generator Network-based abstractive summarization to generate the final meta-review. The focus of our approach is to give a concise meta-review that maximizes information coverage, coherence, readability and also reduces redundancy. Extensive experiments conducted on the PeerRead dataset demonstrate good consistency between the recommended decisions and original decisions. We also compare the performance of MRGen with some of the existing state-of-the-art multi-document summarization methods. The system also outperforms a few existing models based on accuracy, Rouge scores, readability, non-redundancy, and cohesion.

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

Topic modeling combined with classification technique for extractive multi-document text summarization

The qualities of human readable summaries available in the datasets are not up to the mark, leading to issues in creating an accurate model for text summarization. Although recent works have been largely built upon this issue and set up a strong platform for further improvements, they still have many limitations. Looking in this direction, the paper proposes a novel methodology for summarizing a corpus of documents to generate a coherent summary using topic modeling and classification technique. The objectives of the propose work are highlighted below: The outcomes of the empirical work show that the proposed model is more promising compared to the well-known text summarization models.

An unsupervised method for extractive multi-document summarization based on centroid approach and sentence embeddings

Extractive multi-document summarization (MDS) is the process of automatically summarizing a collection of documents by ranking sentences according to their importance and informativeness. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Word embedding representations have shown to be effective for several Natural Language Processing (NLP) tasks including Automatic Text Summarization (ATS). However, most of these representations do not consider the order and the semantic relationships between words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose an unsupervised method for generic extractive multi-document summarization based on the sentence embedding representations and the centroid approach. The proposed method selects relevant sentences according to the final score obtained by combining three scores: sentence content relevance, sentence novelty, and sentence position scores. The sentence content relevance score is computed as the cosine similarity between the centroid embedding vector of the cluster of documents and the sentence embedding vectors. The sentence novelty metric is explicitly adopted to deal with redundancy. The sentence position metric assumes that the first sentences of a document are more relevant to the summary, and it assigns high scores to these sentences. Moreover, this paper provides a comparative analysis of nine sentence embedding models used to represent sentences as dense vectors in a low dimensional vector space in the context of extractive multi-document summarization. Experiments are performed on the standard DUC’2002–2004 benchmark datasets and the Multi-News dataset. The overall obtained results have shown that our method outperforms several state-of-the-art methods and achieves promising results compared to the best performing methods including supervised deep learning based methods.

ISSE: a new iterative sentence scoring and extraction scheme for automatic text summarization

An ever-increasing volume of contextual data appearing on various types of media overwhelms users sharing information, as well as bringing up storage concerns. Automatic text summarization mitigates these challenges and helps users cope more efficiently by generating more accurate and representative summaries. In this paper, we propose a new extractive, multi-document summarization method based on a new iterative sentence scoring and extraction scheme which uses the lexical chain and clustering concepts. The evaluation is done by a well-known standard evaluator in terms of conventional measures of performance, Recall, Precision, and F-score. The results show definite improvement at reasonable compression rates of around 10%, compared to other major benchmark summarizers in the literature.

Un estudio de los métodos de reducción del frente de Pareto a una única solución aplicado al problema de resumen extractivo multi-documento

Automatic summarization methods are currently needed in many different contexts. The extractive multi-document summarization problem tries to cover the main content of a document collection and to reduce the redundant information. The best way to address this task is through a multi-objective optimization approach. The result of this approach is a set of non-dominated solutions or Pareto set. However, since only one summary is needed, the Pareto front must be reduced to a single solution. For this, several methods have been considered, such as the largest hypervolume, the consensus solution, the shortest distance to the ideal point, and the shortest distance to all points. The methods have been tested using datasets from DUC, and they have been evaluated with ROUGE metrics. The results show that consensus solution achieves the best average values.

Unsupervised extractive multi-document text summarization using a genetic algorithm

The task of Extractive Multi-Document Text Summarization (EMDTS) aims at building a short summary with essential information from a collection of documents. In this paper, we propose an EMDTS method using a Genetic Algorithm (GA). The fitness function considering two unsupervised text features: sentence position and coverage. We propose the binary coding representation, selection, crossover, and mutation operators. We test the proposed method on the DUC01 and DUC02 data set, four different tasks (summary lengths 200 and 400 words), for each of the collections of documents (in total, 876 documents) are tested. Besides, we analyze the most frequently used methodologies to summarization. Moreover, different heuristics such as topline, baseline, baseline-random, and lead baseline are calculated. In the results, the proposed method achieves to improve the state-of-art results.

An online multi-source summarization algorithm for text readability in topic-based search

Web search users are likely to face problems related to the availability of large amounts of data. As the quantity of online content grows, the risk of missing relevant information during search can only increase. Moreover, external variables such as the users’ reading proficiency level can further complicate the task.This article proposes an online multi-document summarization algorithm for text readability, as a means to simplify web search. The algorithm is designed to work over collections of topic-related documents, such as the ones returned as the results to a web query. Contrary to most modern approaches, no preliminary training for the algorithm is required.The algorithm was tested in both English and Spanish language documents, using different metrics of term and sentence relevance. The results were compared against summaries created by both human summarizers and third-party Automatic Text Summarization (ATS) systems in terms of two variables: readability and information content.In both variables, the results show generalized gains with respect to both the human summarizers and the third-party ATS systems. Furthermore, the algorithm achieved these results with a time complexity strictly lower than O(n2); well below traditional machine learning approaches.

MSCSO: Extractive Multi-document Summarization Based on a New Criterion of Sentences Overlapping

Extractive multi-document summarization receives a set of documents and extracts the important sentences to form a summary. This paper proposes a novel multi-document summarization with sentences overlapping. First, we preprocess multi-document and calculate 12 features of each sentence. This paper suggests four new features: ROUGE-1 and ROUGE-2 score between the sentence and a single document, ROUGE-1 and ROUGE-2 score between the sentence and multiple documents, also a new definition of sentence overlapping feature. Then, we assign each sentence a score by the learned model. We calculate pairwise overlapping between the sentences and finally select the sentences with higher score and less redundancy. These sentences are given to form the final summary to output under a length constraint. Our method is language free, and it can be implemented on other languages with minor changes. The proposed method is tested on DUC 2006 and 2007 datasets. The effectiveness of this technique is measured using the ROUGE score, and the results are promising when they have been compared with some existing methods.

Extractive Multi-Document Summarization Model Based On Different Integrations of Double Similarity Measures

Currently, the prominence of automatic multi document summarization task belongs to the information rapid increasing on the Internet. Automatic document summarization technology is progressing and may offer a solution to the problem of information overload. 
 Automatic text summarization system has the challenge of producing a high quality summary. In this study, the design of generic text summarization model based on sentence extraction has been redirected into a more semantic measure reflecting individually the two significant objectives: content coverage and diversity when generating summaries from multiple documents as an explicit optimization model. The proposed two models have been then coupled and defined as a single-objective optimization problem. Also, for improving the performance of the proposed model, different integrations concerning two similarity measures have been introduced and applied to the proposed model along with the single similarity measures that are based on using Cosine, Dice and similarity measures for measuring text similarity. For solving the proposed model, Genetic Algorithm (GA) has been used. Document sets supplied by Document Understanding Conference 2002 ( ) have been used for the proposed system as an evaluation dataset. Also, as an evaluation metric, Recall-Oriented Understudy for Gisting Evaluation ( ) toolkit has been used for performance evaluation of the proposed method. Experimental results have illustrated the positive impact of measuring text similarity using double integration of similarity measures against single similarity measure when applied to the proposed model wherein the best performance in terms of and has been recorded for the integration of Cosine similarity and similarity.

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.

A semantic approach to extractive multi-document summarization: Applying sentence expansion for tuning of conceptual densities

Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate performance. Hence, it seems vital to bridge the gap between short text units and conventional text processing methods.In this study, we propose a semantic method for implementing an extractive multi-document summarizer system by using a combination of statistical, machine learning based, and graph-based methods. It is a language-independent and unsupervised system. The proposed framework learns the semantic representation of words from a set of given documents via word2vec method. It expands each sentence through an innovative method with the most informative and the least redundant words related to the main topic of sentence. Sentence expansion implicitly performs word sense disambiguation and tunes the conceptual densities towards the central topic of each sentence. Then, it estimates the importance of sentences by using the graph representation of the documents. To identify the most important topics of the documents, we propose an inventive clustering approach. It autonomously determines the number of clusters and their initial centroids, and clusters sentences accordingly. The system selects the best sentences from appropriate clusters for the final summary with respect to information salience, minimum redundancy, and adequate coverage.A set of extensive experiments on DUC2002 and DUC2006 datasets was conducted for investigating the proposed scheme. Experimental results showed that the proposed sentence expansion algorithm and clustering approach could considerably enhance the performance of the summarization system. Also, comparative experiments demonstrated that the proposed framework outperforms most of the state-of-the-art summarizer systems and can impressively assist the task of extractive text summarization.

Modern Multi-Document Text Summarization Techniques

Text Summarization is the technique in which the source document is simplified, valuable information is distilled and an abridged version is produced. Over the last decade, the focus has shifted from single document to multi-document summarization and despite significant progress in the domain, challenges such as sentence ordering and fluency remain. In this paper, a thorough comparison of the several multi-document text summarization techniques such as Machine Learning based, Graph based, Game-Theory based and more has been presented. This paper in its entirety condenses and interprets the numerous approaches, merits and limitations of these techniques. The Benchmark datasets of this domain and their features have also been examined. This survey aims to distinguish the various summarization algorithms based on properties that prove to be valuable in the generation of highly consistent, rational, summaries with reduced redundancy and information richness. The conclusions presented by this paper can be utilized to identify the advantages of these papers which will help future researchers in their study of this domain and ensure the provision of important data for further analysis in a more systematic and comprehensive manner. With the aid of this paper, researchers can identify the areas that present some scope for improvement and thereafter come up with novel or possibly hybrid techniques in Multi-Document Summarization.

Extractive multi-document summarization using relative redundancy and coherence scores

Extractive multi-document summarization using relative redundancy and coherence scores

Enhancing Pointer Network for Sentence Ordering with Pairwise Ordering Predictions

Dominant sentence ordering models use a pointer network decoder to generate ordering sequences in a left-to-right fashion. However, such a decoder only exploits the noisy left-side encoded context, which is insufficient to ensure correct sentence ordering. To address this deficiency, we propose to enhance the pointer network decoder by using two pairwise ordering prediction modules: The FUTURE module predicts the relative orientations of other unordered sentences with respect to the candidate sentence, and the HISTORY module measures the local coherence between several (e.g., 2) previously ordered sentences and the candidate sentence, without the influence of noisy left-side context. Using the pointer mechanism, we then incorporate this dynamically generated information into the decoder as a supplement to the left-side context for better predictions. On several commonly-used datasets, our model significantly outperforms other baselines, achieving the state-of-the-art performance. Further analyses verify that pairwise ordering predictions indeed provide extra useful context as expected, leading to better sentence ordering. We also evaluate our sentence ordering models on a downstream task, multi-document summarization, and the summaries reordered by our model achieve the best coherence scores. Our code is available at https://github.com/DeepLearnXMU/Pairwise.git.