Automatic Multi-document Summarization Research Articles

An indicator-based multi-objective variable neighborhood search approach for query-focused summarization

Currently, automatic multi-document summarization is an interesting subject in numerous fields of study. As a part of it, query-focused summarization is becoming increasingly important in recent times. These methods can automatically produce a summary based on a query given by the user, including the most relevant information from the query at the same time as the redundancy among sentences is reduced. This can be achieved by developing and applying a multi-objective optimization approach. In this paper, an Indicator-based Multi-Objective Variable Neighborhood Search (IMOVNS) algorithm has been designed, implemented, and tested for the query-focused extractive multi-document summarization problem. Experiments have been carried out with datasets from Text Analysis Conference (TAC). The results were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. IMOVNS has greatly improved the results presented in the scientific literature, providing improvement percentages in ROUGE metric reaching up to 69.24% in ROUGE-1, up to 57.70% in ROUGE-2, and up to 77.37% in ROUGE-SU4 scores. Hence, the proposed IMOVNS offers a promising solution to the query-focused summarization problem, thus highlighting its efficacy and potential for enhancing automatic summarization techniques.

Evaluating a typology of signals for automatic detection of complementarity

In a cluster of news texts on the same event, two sentences from different documents might express different multi-document phenomena (redundancy, complementarity, and contradiction). Cross-Document Structure Theory (CST) provides labels to explicitly represent these phenomena. The automatic identification of the multi-document phenomena and their correspondent CST relations is definitely handy for Automatic Multi-Document Summarization since it helps computers understand text meaning. In this paper, we evaluated a typology of (textual) signals for the automatic detection of the CST relations of complementarity (i.e., Historical background, Follow-up and Elaboration) in a multi-document corpus of news texts in Brazilian Portuguese. Using algorithms from different machine-learning paradigms, we obtained classifiers that achieved high general accuracy (higher than 90%), indicating the potential of the signals.

Optimizing Data-Driven Models for Summarization as Parallel Tasks

This paper presents tackling of a hard optimization problem of computational linguistics, specifically automatic multi-document text summarization, using grid computing. The main challenge of multi-document summarization is to extract the most relevant and unique information effectively and efficiently from a set of topic-related documents, constrained to a specified length. In the Big Data/Text era, where the information increases exponentially, optimization becomes essential in selection of the most representative sentences for generating the best summaries. Therefore, a data-driven summarization model is proposed and optimized during a run of Differential Evolution (DE).Different DE runs are distributed to a grid in parallel as optimization tasks, seeking high processing throughput despite the demanding complexity of the linguistic model, especially on longer multi-documents where DE improves results given more iterations. Namely, parallelization and the grid enable, running several independent DE runs at same time within fixed real-time budget. Such approach results in improving a Document Understanding Conference (DUC) benchmark recall metric over a previous setting.

Deep submodular network: An application to multi-document summarization

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be done in polynomial time and also, by choosing a suitable learning rate and performing enough iterations, a lower empirical error can be ensured. Finally, in order to demonstrate the applicability of DSN for real-world problems, automatic multi-document summarization is considered and a summarizer called DSNSum is introduced. Then, the performance of DSNSum is compared with the state-of-the-art summarizers based on DUC 2004 and CNN/DailyMail corpora. The experimental results show that the performance of the proposed summarizer is comparable with the state-of-the-art methods.

Parallelizing a multi-objective optimization approach for extractive multi-document text summarization

Currently, automatic multi-document text summarization is an important task in many fields of knowledge, due to the continuous exponential growth of information on the Internet. Nevertheless, this task is computationally demanding. In the last years, automatic text summarization has been addressed by using multi-objective optimization approaches. In particular, recently, the Multi-Objective Artificial Bee Colony (MOABC) algorithm has obtained very good results. This work focuses on the parallelization of this approach. Several steps have been carried out for this goal. After a time profiling of the algorithm, a runtime comparison has been performed between the use of different random number generators within the algorithm. Then, a parallel implementation of the MOABC algorithm has been designed following its original scheme, in which the main steps are parallelized, and different parallel schedules have been studied and compared. Finally, a second design based on the asynchronous behavior of the bee colony in nature has been implemented and compared. Experiments have been carried out with datasets from Document Understanding Conference (DUC). The results show that the asynchronous design improves greatly the parallel design, being more than 55 times faster with 64 threads than the standard design. An efficiency of 86.72% has been reported for 64 threads.

Automatic Multi-Document Summarization Based on Keyword Density and Sentence-Word Graphs

As a fundamental and effective tool for document understanding and organization, multi-document summarization enables better information services by creating concise and informative reports for large collections of documents. In this paper, we propose a sentence-word two layer graph algorithm combining with keyword density to generate the multi-document summarization, known as Graph & Keywordρ. The traditional graph methods of multi-document summarization only consider the influence of sentence and word in all documents rather than individual documents. Therefore, we construct multiple word graph and extract right keywords in each document to modify the sentence graph and to improve the significance and richness of the summary. Meanwhile, because of the differences in the words importance in documents, we propose to use keyword density for the summaries to provide rich content while using a small number of words. The experiment results show that the Graph & Keywordρ method outperforms the state of the art systems when tested on the Duc2004 data set.

Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization

Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distribution is suitable for obtaining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or absence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi-document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.

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.

Study on Multi-document Summarization Based on Text Segmentation

This paper introduces a novel approach of automatic multi-document summarization based on text segmentation. The approach acquires concepts with a How-Net oriented tool and calculates the importance degree of sentences by means of employing the improved DotPlotting model and establishing a sentence-based vector space model (VSM). A conclusion is made according to the importance degree and similarity of sentences. Experimental results show that the performance on ROUGE of the approach put forward hereby is effective and significant.

Using AdaBoost Meta-Learning Algorithm for Medical News Multi-Document Summarization

Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.

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.

Automatic Multi-document Summarization Based on Clustering and Nonnegative Matrix Factorization

In this paper, a novel summarization method that uses nonnegative matrix factorization (NMF) and the clustering method is introduced to extract meaningful sentences relevant to a given query. The proposed method decomposes a sentence into the linear combination of sparse nonnegative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can improve the quality of document summaries because it can avoid extracting those sentences whose similarities with the query are high but that are meaningless by using the similarity between the query and the semantic features. In addition, the proposed approach uses the clustering method to remove noise and avoid the biased inherent semantics of the documents being reflected in summaries. The method can ensure the coherence of summaries by using the rank score of sentences with respect to semantic features. The experimental results demonstrate that the proposed method has better performance than other methods that use the thesaurus, the latent semantic analysis (LSA), the K-means, and the NMF.

Automatic multidocument summarization of research abstracts: Design and user evaluation

AbstractThe purpose of this study was to develop a method for automatic construction of multidocument summaries of sets of research abstracts that may be retrieved by a digital library or search engine in response to a user query. Sociology dissertation abstracts were selected as the sample domain in this study. A variable‐based framework was proposed for integrating and organizing research concepts and relationships as well as research methods and contextual relations extracted from different dissertation abstracts. Based on the framework, a new summarization method was developed, which parses the discourse structure of abstracts, extracts research concepts and relationships, integrates the information across different abstracts, and organizes and presents them in a Web‐based interface. The focus of this article is on the user evaluation that was performed to assess the overall quality and usefulness of the summaries. Two types of variable‐based summaries generated using the summarization method—with or without the use of a taxonomy—were compared against a sentence‐based summary that lists only the research‐objective sentences extracted from each abstract and another sentence‐based summary generated using the MEAD system that extracts important sentences. The evaluation results indicate that the majority of sociological researchers (70%) and general users (64%) preferred the variable‐based summaries generated with the use of the taxonomy.

Constructing a taxonomy to support multi-document summarization of dissertation abstracts

This paper reports part of a study to develop a method for automatic multi-document summarization. The current focus is on dissertation abstracts in the field of sociology. The summarization method uses macro-level and micro-level discourse structure to identify important information that can be extracted from dissertation abstracts, and then uses a variable-based framework to integrate and organize extracted information across dissertation abstracts. This framework focuses more on research concepts and their research relationships found in sociology dissertation abstracts and has a hierarchical structure. A taxonomy is constructed to support the summarization process in two ways: (1) helping to identify important concepts and relations expressed in the text, and (2) providing a structure for linking similar concepts in different abstracts. This paper describes the variable-based framework and the summarization process, and then reports the construction of the taxonomy for supporting the summarization process. An example is provided to show how to use the constructed taxonomy to identify important concepts and integrate the concepts extracted from different abstracts.