Multi-document Summarization Research Articles

Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization

Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the document-dependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.

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.

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

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

Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms

Over the last years, several Multi-Document Summarization (MDS) methods have been presented in Document Understanding Conference (DUC) workshops. Since DUC01, several methods have been presented in approximately 268 publications of the state-of-the-art, that have allowed the continuous improvement of MDS, however in most works the upper bounds were unknowns. Recently, some works have been focused to calculate the best sentence combinations of a set of documents and in previous works we have been calculated the significance for single-document summarization task in DUC01 and DUC02 datasets. However, for MDS task has not performed an analysis of significance to rank the best multi-document summarization methods. In this paper, we propose a method based on Genetic Algorithms for calculating the best sentence combinations of DUC01 and DUC02 datasets in MDS through a meta-document representation. Moreover, we have calculated three heuristics mentioned in several works of state-of-the-art to rank the most recent MDS methods, through the calculus of upper bounds and lower bounds.

BHLM: Bayesian theory-based hybrid learning model for multi-document summarization

In order to understand and organize the document in an efficient way, the multi-document summarization becomes the prominent technique in the Internet world. As the information available is in a large amount, it is necessary to summarize the document for obtaining the condensed information. To perform the multi-document summarization, a new Bayesian theory-based Hybrid Learning Model (BHLM) is proposed in this paper. Initially, the input documents are preprocessed, where the stop words are removed from the document. Then, the feature of the sentence is extracted to determine the sentence score for summarizing the document. The extracted feature is then fed into the hybrid learning model for learning. Subsequently, learning feature, training error and correlation coefficient are integrated with the Bayesian model to develop BHLM. Also, the proposed method is used to assign the class label assisted by the mean, variance and probability measures. Finally, based on the class label, the sentences are sorted out to generate the final summary of the multi-document. The experimental results are validated in MATLAB, and the performance is analyzed using the metrics, precision, recall, [Formula: see text]-measure and rouge-1. The proposed model attains 99.6% precision and 75% rouge-1 measure, which shows that the model can provide the final summary efficiently.

Extractive multi-document summarization using multilayer networks

Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in the context of an extractive multi document summarization (MDS) task. In the adopted model, nodes represent sentences and edges are created based on the number of shared words between sentences. Differently from previous studies in multi-document summarization, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer). As a proof of principle, our results reveal that such a discrimination between intra- and inter-layer in a multilayered representation is able to improve the quality of the generated summaries. This piece of information could be used to improve current statistical methods and related textual models.

Multi-documents summarization based on clustering of learning object using hierarchical clustering

The Open Educational Resources (OER) is a portal of teaching, learning and research resources that is available in public domain and freely accessible. Learning contents or Learning Objects (LO) are granular and can be reused for constructing new learning materials. LO ontology-based searching techniques can be used to search for LO in the Indonesia OER. In this research, LO from search results are used as an ingredient to create new learning materials according to the topic searched by users. Summarizing-based grouping of LO use Hierarchical Agglomerative Clustering (HAC) with the dependency context to the user’s query which has an average value F-Measure of 0.487, while summarizing by K-Means F-Measure only has an average value of 0.336.

Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.

Multi-Document Text Summarization Using Deep Learning Algorithm with Fuzzy Logic

Multi-document text summarization focuses on extracting the key information from the collection of related documents and presents it as a brief summary.In this paper, we are presenting multi document text summarization using Deep learning algorithm with fuzzy logic which is an important research area in NLP, data mining(DM) and Machine Learning(ML). To improve the accuracy here , we are using Restricted Boltzman machine to generate a shortend version of original document without losing its valuable information. The current method consists of two steps: 1.Training phase 2. Testing phase. The prominent role of the training phase is to extract effective summary generation. Afterwards the testing phase is implemented to validate the efficiency and accuracy of the proposed method.

Multi-Document Summarization Using K-Means and Latent Dirichlet Allocation (LDA) – Significance Sentences

The contents of online news documents are almost the same that will lead to the redundancy of news or called yellow journalism. Yellow journalism can make it difficult for readers to distinguish documents containing fact or opinionated information. Therefore, it is necessary to extend more research about multi-document summarization so that readers can easily understand the intent of online news documents. Latent Dirichlet Allocation (LDA) - Significance Sentences is one of the methods for summarization, which performs better than the term frequency algorithm. However, document summarization using the method is only able to summarize multiple documents as a whole without grouping by topic. Subsequently, it can give an unorganized summary result. Therefore, this research proposes a novel summarization method which combines K-Means Clustering and LDA - Significance Sentences, so it can generate document summaries based on the topic. We implemented two scenarios of the experiment. The first experimental results the best alpha value is 0.001 with the ROUGE-1 value of 0.5545 and the best summarization level is 30% with the ROUGE-1 value of 0.6118. While the second experiment results, the best obtain of ROUGE-1 value is 0.61991 for the first cluster which is consists of documents 1, 2, 3, 4, and 6 and 0.6139 for the second cluster which is consists of 5, 7, 8. Multi-document summarization using the proposed method has good performance when the K-Means method can cluster the document according to the topic correctly, which is highly dependent on the accuracy of determining the initial centroid.

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization problem, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and some existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.

QMOS: Query-based multi-documents opinion-oriented summarization

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews.QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon.On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.

Exploring content selection strategies for Multilingual Multi-Document Summarization based on the Universal Network Language (UNL)

Abstract: Multilingual Multi-Document Summarization aims at ranking the sentences of a cluster with (at least) 2 news texts (1 in the user’s language and 1 in a foreign language), and select the top-ranked sentences for a summary in the user’s language. We explored three concept-based statistics and one superficial strategy for sentence ranking. We used a bilingual corpus (Brazilian Portuguese-English) encoded in UNL (Universal Network Language) with source and summary sentences aligned based on content overlap. Our experiment shows that “concept frequency normalized by the number of concepts in the sentence” is the measure that best ranks the sentences selected by humans. However, it does not outperform the superficial strategy based on the position of the sentences in the texts. This indicates that the most frequent concepts are not always contained in first sentences, usually selected by humans to build the summaries because they convey the main information of the collection.Keywords: content selection; concept; statistical measure; multilingual corpus; multi-document summarization.Keywords: content selection; concept; statistical measure; multilingualcorpus; multi-document summarization.Resumo: O objetivo da Sumarização Automática Multilíngue Multidocumento é ranquear as sentenças de uma coleção com ao menos duas notícias (1 na língua do usuário e 1 em língua estrangeira) e selecionar as mais bem pontuadas para compor um sumário na língua do usuário. Exploramos três estatísticas conceituais e uma estratégia superficial para criar um ranque das sentenças quanto à relevância. Para tanto, utilizamos um corpus bilíngue (português-inglês) anotado via UNL (Universal Network Language) e com textos-fonte e sumários alinhados em nível sentencial. A avaliação indica que a estatísticadenominada frequência de conceitos normalizada pelo número de conceitos da sentença é a que melhor reproduz o ranqueamento humano. Essa medida, entretanto, não supera a estratégia superficial baseada na posição das sentenças. Isso indica que os conceitos mais frequentes do cluster nem sempre estão contidos nas primeiras sentenças dos textosfonte, usualmente selecionadas pelos humanos para compor os sumários porque veiculam a informação principal da coleção.Palavras-chave: seleção de conteúdo; conceito; medida estatística; corpus multilíngue; sumarização multidocumento.

Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach

Automatic text summarization methods are increasingly needed nowadays. Extractive multi-document summarization approaches aim to obtain the main content of a document collection at the same time that the redundant information is reduced. This can be addressed from an optimization point of view. There is a lack of multi-objective approaches applied in this context. In this paper, a Multi-Objective Artificial Bee Colony (MOABC) algorithm has been designed and implemented for this task. Experiments have been performed based on datasets from Document Understanding Conference (DUC) and model performances have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, as is usual in this knowledge field. The results of the proposed approach show important improvements, i.e., in average, 31.09% (8.43%) and 18.63% (6.09%) of improvement in ROUGE-2 (ROUGE-L) have been obtained with respect to the best single-objective and multi-objective results in the scientific literature. Even more, the proposed approach has been proven to produce more concentrated ROUGE values when the algorithm execution is repeated (between 620.63% and 1333.95% of reduction in the relative dispersion, that is, between 6 and 13 times better), leading to more robust results.

Multi-document summarization based on sentence cluster using non-negative matrix factorization

Multi-document summarization based on sentence cluster using non-negative matrix factorization

Event-based knowledge reconciliation using frame embeddings and frame similarity

This paper proposes an evolution over MERGILO, a tool for reconciling knowledge graphs extracted from text, using graph alignment and word similarity. The reconciled knowledge graphs are typically used for multi-document summarization, or to detect knowledge evolution across document series. The main point of improvement focuses on event reconciliation i.e., reconciling knowledge graphs generated by text about two similar events described differently. In order to gather a complete semantic representation of events, we use FRED semantic web machine reader, jointly with Framester, a linguistic linked data hub represented using a novel formal semantics for frames. Framester is used to enhance the extracted event knowledge with semantic frames. We extend MERGILO with similarities based on the graph structure of semantic frames and the subsumption hierarchy of semantic roles as defined in Framester. With an effective evaluation strategy similarly as used for MERGILO, we show the improvement of the new approach (MERGILO plus semantic frame/role similarities) over the baseline.

Personalised health document summarisation exploiting Unified Medical Language System and topic-based clustering for mobile healthcare

According to the growing interest in mobile healthcare, multi-document summarisation techniques are increasingly required to cope with health information overload and effectively deliver personalised online healthcare information. However, because of the peculiarities of medical terminology and the diversity of subtopics in health documents, multi-document summarisation must consider technical aspects that are different from those of the general domain. In this article, we propose a personalised health document summarisation system that provides a reliable personal health-related summary to general healthcare consumers via mobile devices. Our system generates a personalised summary from multiple online health documents by exploiting biomedical concepts, semantic types and semantic relations extracted from the Unified Medical Language System (UMLS) and analysing individual health records derived from mobile personal health record (PHR) applications. Furthermore, to increase the diversity and coverage of summarised results and to display them in a user-friendly manner on mobile devices, we create a summary that is categorised into subtopics by grouping semantically related sentences through topic-based clustering. The experimental evaluations demonstrate the effectiveness of our proposed system.

TOPSIS with Multiple Linear Regression for Multi-Document Text Summarization

TOPSIS with Multiple Linear Regression for Multi-Document Text Summarization

Text summarization from legal documents: a survey

Enormous amount of online information, available in legal domain, has made legal text processing an important area of research. In this paper, we attempt to survey different text summarization techniques that have taken place in the recent past. We put special emphasis on the issue of legal text summarization, as it is one of the most important areas in legal domain. We start with general introduction to text summarization, briefly touch the recent advances in single and multi-document summarization, and then delve into extraction based legal text summarization. We discuss different datasets and metrics used in summarization and compare performances of different approaches, first in general and then focused to legal text. we also mention highlights of different summarization techniques. We briefly cover a few software tools used in legal text summarization. We finally conclude with some future research directions.

Linguistic challenges in automatic summarization technology

Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.