arXiv subjects

Milad Moradi

Publications and source records attributed to Milad Moradi.

25 records · Page 2Linked to original sources

arXiv2019

Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts

In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts. However, construction and maintenance of domain knowledge bases are resource-intense tasks requiring significant manual annotation. In this paper, we demonstrate that contextualized representations extracted from the pre-trained deep language model BERT, can be effectively used to measure the similarity between sentences and to quantify the informative content. The results show that our BERT-based summarizer can improve the performance of biomedical summarization. Although the summarizer does not use any sources of domain knowledge, it can capture the context of sentences more accurately than the comparison methods. The source code and data are available at https://github.com/BioTextSumm/BERT-based-Summ.

cs.CL↗

arXiv2019

Text Summarization in the Biomedical Domain

This chapter gives an overview of recent advances in the field of biomedical text summarization. Different types of challenges are introduced, and methods are discussed concerning the type of challenge that they address. Biomedical literature summarization is explored as a leading trend in the field, and some future lines of work are pointed out. Underlying methods of recent summarization systems are briefly explained and the most significant evaluation results are mentioned. The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization. As the main parts of this chapter, current trends are discussed and new challenges are introduced.

cs.CL↗

arXiv2019

Small-world networks for summarization of biomedical articles

In recent years, many methods have been developed to identify important portions of text documents. Summarization tools can utilize these methods to extract summaries from large volumes of textual information. However, to identify concepts representing central ideas within a text document and to extract the most informative sentences that best convey those concepts still remain two crucial tasks in summarization methods. In this paper, we introduce a graph-based method to address these two challenges in the context of biomedical text summarization. We show that how a summarizer can discover meaningful concepts within a biomedical text document using the Helmholtz principle. The summarizer considers the meaningful concepts as the main topics and constructs a graph based on the topics that the sentences share. The summarizer can produce an informative summary by extracting those sentences having higher values of the degree. We assess the performance of our method for summarization of biomedical articles using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit. The results show that the degree can be a useful centrality measure to identify important sentences in this type of graph-based modelling. Our method can improve the performance of biomedical text summarization compared to some state-of-the-art and publicly available summarizers. Combining a concept-based modelling strategy and a graph-based approach to sentence extraction, our summarizer can produce summaries with the highest scores of informativeness among the comparison methods. This research work can be regarded as a start point to the study of small-world networks in summarization of biomedical texts.

cs.CL↗

arXiv2017

A Method for Finding Similar Documents Relying on Adding Repetition of Symbols in Length Based Filtering

A basic topic in mining of massive dataset is finding similar items. As an example, finding similar documents can be recommended. In this case many methods are existed. For example, Shingling method and length based filtering are one of them. In Shingling method, from each document, substrings have been selected with symbol name and, they are placed on one set. For finding similar documents, the similarities of sets that related with them have been calculated. In Length based filtering just documents which close these lengths have been compared. These methods don't consider repetition of symbols. With considering the repetition can calculate length of documents with more accurately. In this paper we suggested a method for finding similar documents with considering the repetition of symbols. This method separated documents to better form. The main goal of this paper is presentation a method for finding similar documents with take fewer comparisons and time indeed.

cs.IR↗

arXiv2017

Different approaches for identifying important concepts in probabilistic biomedical text summarization

Automatic text summarization tools help users in biomedical domain to acquire their intended information from various textual resources more efficiently. Some of the 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 frequency for identifying the valuable content of the input document, and considering the correlations existing between concepts may be more useful for this type of summarization. In this paper, we describe a Bayesian summarizer 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 different feature selection approaches to identify the most important concepts of the text and to select the most informative content according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian biomedical summarizer can improve the performance of summarization. We perform extensive evaluations on a corpus of scientific papers in biomedical domain. The results show that the Bayesian summarizer outperforms the biomedical summarizers that rely on the frequency of concepts, the domain-independent and baseline methods based on the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step lead to a significant increase in the performance of summarization.

cs.CL↗

arXiv2017

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

Objective: Automatic 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. Methods: To address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the 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. Results: We evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. 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 . Conclusion: Compared 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.

cs.IR↗

arXiv2016

A centralized reinforcement learning method for multi-agent job scheduling in Grid

One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability.

cs.DC↗