Recall-Oriented Understudy For Gisting Evaluation Research Articles

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

ObjectiveAutomatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. MethodsOur summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. ResultsWe perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. ConclusionThe carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain.

A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms

Text summarization is the process of producing a shorter version of a specific text. Automatic summarization techniques have been applied to various domains such as medical, political, news, and legal domains proving that adapting domain-relevant features could improve the summarization performance. Despite the existence of plenty of research work in the domain-based summarization in English and other languages, there is a lack of such work in Arabic due to the shortage of existing knowledge bases. In this paper, a hybrid, single-document text summarization approach (abbreviated as (ASDKGA)) is presented. The approach incorporates domain knowledge, statistical features, and genetic algorithms to extract important points of Arabic political documents. The ASDKGA approach is tested on two corpora KALIMAT corpus and Essex Arabic Summaries Corpus (EASC). The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework was used to compare the automatically generated summaries by the ASDKGA approach with summaries generated by humans. Also, the approach is compared against three other Arabic text summarization approaches. The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.

Natural Language Description of Video Streams Using Task-Specific Feature Encoding

In recent years, deep learning approaches have gained great attention due to their superior performance and the availability of high speed computing resources. These approaches are also extended towards the real time processing of multimedia content exploiting its spatial and temporal structure. In this paper, we propose a deep learning-based video description framework which first extracts visual features from video frames using deep convolutional neural networks (CNN) and then pass the derived representations into a long-short term memory-based language model. In order to capture accurate information for human presence, a fine-tuned multi-task CNN is presented. The proposed pipeline is end-to-end, trainable, and capable of learning dense visual features along with an accurate framework for the generation of natural language descriptions of video streams. The evaluation is done by calculating Metric for Evaluation of Translation with Explicit ORdering and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores between system generated and human annotated video descriptions for a carefully designed data set. The video descriptions generated by the traditional feature learning and proposed deep learning frameworks are also compared through the ROUGE scores.

Different approaches for identifying important concepts in probabilistic biomedical text summarization.

Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the raw frequency for identifying valuable contents within an input document, or considering correlations existing between concepts, may be more useful for this type of summarization. In this paper, we describe a Bayesian summarization method for biomedical text documents. The Bayesian summarizer initially maps the input text to the Unified Medical Language System (UMLS) concepts; then it selects the important ones to be used as classification features. We introduce six different feature selection approaches to identify the most important concepts of the text and select the most informative contents according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian summarizer can improve the performance of biomedical summarization. Using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit, we perform extensive evaluations on a corpus of scientific papers in the biomedical domain. The results show that when the Bayesian summarizer utilizes the feature selection methods that do not use the raw frequency, it can outperform the biomedical summarizers that rely on the frequency of concepts, domain-independent and baseline methods.

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.

Quantifying the informativeness for biomedical literature summarization: An itemset mining method

ObjectiveAutomatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our summarizer quantifies the informativeness of each sentence using the support values of itemsets appearing in the sentence. MethodsTo address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the Unified Medical Language System (UMLS). Then, it discovers the essential subtopics of the text using a data mining technique, namely itemset mining, and constructs the summarization model. The employed itemset mining algorithm extracts a set of frequent itemsets containing correlated and recurrent concepts of the input document. The summarizer selects the most related and informative sentences and generates the final summary. ResultsWe evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. We compare the proposed method with GraphSum, TexLexAn, SweSum, SUMMA, AutoSummarize, the term-based version of the itemset-based summarizer, and two baselines. The results show that the itemset-based summarizer performs better than the compared methods. The itemset-based summarizer achieves the best scores for all the assessed ROUGE metrics (R-1: 0.7583, R-2: 0.3381, R-W-1.2: 0.0934, and R-SU4: 0.3889). We also perform a set of preliminary experiments to specify the best value for the minimum support threshold used in the itemset mining algorithm. The results demonstrate that the value of this threshold directly affects the accuracy of the summarization model, such that a significant decrease can be observed in the performance of summarization due to assigning extreme thresholds. ConclusionCompared to the statistical, similarity, and word frequency methods, the proposed method demonstrates that the summarization model obtained from the concept extraction and itemset mining provides the summarizer with an effective metric for measuring the informative content of sentences. This can lead to an improvement in the performance of biomedical literature summarization.

An Ontology-based Summarization System for Arabic Documents (OSSAD)

With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user’s query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.

A semantic graph-based approach to biomedical summarisation

Access to the vast body of research literature that is available in biomedicine and related fields may be improved by automatic summarisation. This paper presents a method for summarising biomedical scientific literature that takes into consideration the characteristics of the domain and the type of documents. To address the problem of identifying salient sentences in biomedical texts, concepts and relations derived from the Unified Medical Language System (UMLS) are arranged to construct a semantic graph that represents the document. A degree-based clustering algorithm is then used to identify different themes or topics within the text. Different heuristics for sentence selection, intended to generate different types of summaries, are tested. A real document case is drawn up to illustrate how the method works. A large-scale evaluation is performed using the recall-oriented understudy for gisting-evaluation (ROUGE) metrics. The results are compared with those achieved by three well-known summarisers (two research prototypes and a commercial application) and two baselines. Our method significantly outperforms all summarisers and baselines. The best of our heuristics achieves an improvement in performance of almost 7.7 percentage units in the ROUGE-1 score over the LexRank summariser (0.7862 versus 0.7302). A qualitative analysis of the summaries also shows that our method succeeds in identifying sentences that cover the main topic of the document and also considers other secondary or "satellite" information that might be relevant to the user. The method proposed is proved to be an efficient approach to biomedical literature summarisation, which confirms that the use of concepts rather than terms can be very useful in automatic summarisation, especially when dealing with highly specialised domains.

Metodologia de acesso a dissertações de mestrado de tradução por estrangeiros, uma abordagem preliminar

O artigo aborda a primeira etapa de uma proposta metodológica para facilitar o acesso de estrangeiros a dissertações de mestrado escritas em português disponíveis em repositórios digitais. Essa etapa consiste na criação e formatação para tratamento computacional dos corpora de dissertações, na seleção de um sumarizador automatico da língua portuguesa, no processamento das sumarizações automáticas e na comparação dos sumários assim obtidos com os resumos das dissertações. São também apresentadas e analisadas as métricas obtidas através do programa ROUGE (Recall-Oriented Understudy for Gisting Evaluation). É mencionada igualmente a etapa final do trabalho (que fará uso de tecnologias da língua tais como tradução auxiliada por computador, alinhamento de textos), a partir do corpus ora construído, a ser usado para completar o desenvolvimento da metodologia proposta, a qual poderá ser testada em salas de referência de bibliotecas.