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Objectives: Extracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process., Methods: We developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications. The summaries at the sentence and fragment levels were evaluated in finding common clinical SR data elements such as sample size, group size, and PICO values. We compared the computer-generated summaries with human written summaries (title and abstract) in terms of the presence of necessary information for the data extraction as presented in the Cochrane review's study characteristics tables., Results: At the sentence level, the computer-generated summaries covered more information than humans do for systematic reviews (recall 91.2% vs. 83.8%, p<0.001). They also had a better density of relevant sentences (precision 59% vs. 39%, p<0.001). At the fragment level, the ensemble approach combining rule-based, concept mapping, and dictionary-based methods performed better than individual methods alone, achieving an 84.7% F-measure., Conclusion: Computer-generated summaries are potential alternative information sources for data extraction in systematic review development. Machine learning and natural language processing are promising approaches to the development of such an extractive summarization system., (Copyright © 2016 Elsevier Inc. All rights reserved.)

A vast amount of textual information on the internet has amplified the importance of text summarization models. Abstractive summarization generates original words and sentences that may not exist in the source document to be summarized. Such abstractive models may suffer from shortcomings such as linguistic acceptability and hallucinations. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) is a metric commonly used to evaluate abstractive summarization models. However, due to its n-gram-based approach, it ignores several critical linguistic aspects. In this work, we propose Similarity, Entailment, and Acceptability Score (SEAScore), an automatic evaluation metric for evaluating abstractive text summarization models using the power of state-of-the-art pre-trained language models. SEAScore comprises three language models (LMs) that extract meaningful linguistic features from candidate and reference summaries and a weighted sum aggregator that computes an evaluation score. Experimental results show that our LM-based SEAScore metric correlates better with human judgment than standard evaluation metrics such as ROUGE-N and BERTScore. [ABSTRACT FROM AUTHOR]

Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework is based on a well defined methodology for performing semantic graph parsing, graph construction, graph transformations for machine learning models and deep learning predictions. The employed semantic graph representation focuses on using the model of abstract meaning representation. Several combinations of graph construction and graph transformation methods are investigated to specify the most efficient of them for the machine learning models. Additionally, a range of deep learning architectures is examined, including a sequence-to-sequence attentive network, reinforcement learning, transformer-based architectures, and pre-trained neural language models. In this direction, a semantic graph representation of an original text is extracted, and then the present framework formulates the problem as a graph-to-summary learning problem to predict a summary of an original text. To the best of our knowledge, this formulation of graph-to-summary predictions in abstractive text summarization, without other intermediate steps in the machine learning phase, has not been presented in the relevant literature. Another important contribution is an introduction of a measure for assessing the factual consistency of the generated summaries in an effort to provide a qualitative evaluation. To assess the framework, an extensive experimental procedure is presented that uses popular datasets to evaluate key aspects of the proposed approach. The obtained results exhibit promising performance, validating the robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

The most recent and precise biological and healthcare knowledge is critical in the current outbreak such as COVID. In today's small world, everyone needs timely and appropriate medical information to prevent contagious diseases. Extraction of important information from medical conversations and dissemination to patients and doctors may benefit in the treatments of doctor tiredness and patient amnesia. Problem: Automatic text summarizing is essential for gaining great knowledge in any topic in an efficient and productive manner. The material included in health records is vital to our understanding of kind illness and its manifestations. Creating a comprehensive and standard kind of content is becoming an unavoidable and crucial problem in the medical process as a result of the massive amounts of fragmented data created in many sectors. Approach: The purpose of this study is to employ NLP‐based deep learning algorithms for text summary that perform well on linguistic text summarization data, and then modify/adapt these for biomedical domain‐specific text summarization. This paper provides an approach developed in‐house for condensing ill‐punctuated or unpunctuated discussion transcripts into more intelligible summaries, which combines topic modelling and phrase selection with punctuation restorations. For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN). Results: Extensive testing, examination, and comparing have demonstrated that this summarizer works well for medical transcript summarization. The suggested approach achieved an average ROUGE score of 93.5% using a single document summary. Furthermore, by comparing new techniques to previous ones, the utility and accuracy of novel strategies would be shown. The results reveal that models trained on ordinary language provide comparable results on a biomedical testing set, with one model outperforming the linguistic test set. [ABSTRACT FROM AUTHOR]

Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation. [ABSTRACT FROM AUTHOR]

Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. Methods: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. Results: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. Conclusion: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization. [ABSTRACT FROM AUTHOR]

In the current digital epoch, the vast expanse of information has revolutionized the accessibility of knowledge and perspectives. Nevertheless, this information abundance has introduced challenges in navigating and comprehending the deluge of textual data. The surge in online news articles, research papers, reports, and diverse document genres has accentuated the necessity for proficient document summarization techniques. Traditional manual methods of summarization are time-intensive and influenced by subjective biases. In contrast, the synergy between Natural Language Processing (NLP) and machine learning has unlocked the potential for automated document summarization, promising efficient information consumption and informed decision-making. This research paper delves into the convergence of these factors. It is driven by the Longformer model's distinctive capability to manage extensive texts while retaining contextual coherence--a potential solution to the hurdle of large document summarization. By capitalizing on the Longformer's architecture, this study endeavors to exploit its prowess in generating cohesive summaries from lengthy source documents, thereby amplifying the accessibility of intricate information. [ABSTRACT FROM AUTHOR]

Retrieving information from evolving digital data collection using a user's query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection. Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing (NLP) which can be used for precise retrieval and are considered to be the best option. NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features. The study aims to propose a hybrid approach for query-based extractive text summarization in the research study. Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals. Query-based text summarization of multiple documents using a hybrid approach, combining the K-Means clustering technique with Latent Dirichlet Allocation (LDA) as topic modeling technique produces 0.288, 0.631, and 0.328 for precision, recall, and F-score, respectively. The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) is the computerized approach to analyzing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyzer (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown. [ABSTRACT FROM AUTHOR]

Making scribes and prescriptions are the primary activities for a health professional to serve the patients. Although in most of the cases these tasks are pursued manually, a few studies focused on developing digital scribe generation and prescription systems. Moreover, to enhance the effectiveness and adoption of such digital scribe and prescription systems, these systems should be intelligent and useable enough. Therefore, the objective of this research is to understand the user requirements for developing an automated scribes and intelligent prescribing system for health professionals and to develop the automated scribes and intelligent prescribing system based on the revealed users' requirements. And finally, to evaluate the performance of the proposed system. To attain these objectives, a requirement elicitation study was carried out following the semi-structured interviews to reveal the user requirements for an intelligent scribe and prescription system. The study proposed an automated digital scribe that can record medical information adopting the LSTM model; and also be able to generate automated prescriptions based on a doctor's voice command. Finally, the system was evaluated through an empirical study where participants (doctors) were asked to generate scribes and provide prescriptions manually and also by using the proposed system. The study found that the scribes and prescriptions generated using the proposed system are highly similar to the scribes (87.5 %) and prescriptions (96.2 %) generated manually. Analysis of the evaluation results also showed that the system provides a user-friendly, easy-to-use, intuitive, and interactive interface to facilitate the doctors and clinicians.

Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often lengthy and complex, making it challenging for users to fully grasp their content. Additionally, existing transparency analytics tools typically rely on the manual extraction of information elements, resulting in a time-consuming process. To address these challenges, this paper proposes a novel approach that combines information visualization and machine learning analyses to automate the retrieval of information elements. The methodology involves the creation and labeling of a dataset derived from multiple software terms of use. Machine learning models, including naïve Bayes, BART, and LSTM, are utilized for the classification of information elements and text summarization. Furthermore, the proposed approach is integrated into our existing visualization tool TranspVis to enable the automatic detection and display of software information elements. The system is thoroughly evaluated using a database-connected tool, incorporating various metrics and expert opinions. The results of our study demonstrate the promising potential of our approach, serving as an initial step in this field. Our solution not only addresses the challenge of extracting information elements from complex terms of service but also provides a foundation for future research in this area. [ABSTRACT FROM AUTHOR]

This research suggests utilizing natural language processing to summarize the YouTube video transcript in a way that preserves important details. Around one-third of YouTube users in India who view videos on their mobile devices spend more than 48 hours each month on the platform, according to a Google study. It has gotten tougher to sit through these films, some of which may go longer than they should. Our efforts can even be in naught if we can't find the information we need in them. It can take a lot of time and effort to find movies that provide the information we are really seeking for. There are a lot of online videos, for instance, where the speaker wants to capture the audience's attention, but we won't understand the message unless we watch the entire video. The built-in model accepts links to YouTube videos and creates summaries for each one. It also gives the user the ability to choose the algorithm and the percentage of summaries they want to create. Another feature that the user has access to is the ability to transform the generated summary from text to speech and to translate it into the Hindi language. [ABSTRACT FROM AUTHOR]

Initiated by the University Consortium of Geographic Information Science (UCGIS), the GIS&T Body of Knowledge (BoK) is a community‐driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re‐organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationships. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches. Besides, a novel text summarization—KACERS (Keyword‐Aware Cross‐Encoder‐Ranking Summarizer)—is proposed to generate a semantic summary of scientific publications. By identifying the semantic linkages among key topics, this work guides the future development and content organization of the GIS&T BoK project. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications and demonstrates the potential of the KACERS summarizer in semantic understanding of long text documents. [ABSTRACT FROM AUTHOR]

The term "Medical Text summarization" refers to the process of extracting or collecting more useful information from medical articles in a concise manner. Every day, the count of medical publications increases continuously, and applying text summarization techniques can minimize the time needed to manually transform medical papers into a summarized version. This study's goal is to present a summary of recent works in medical text summarization from 2018 to 2022. It includes 15 papers covering different methodologies such as Clinical Context-Aware (CCA), Prognosis Quality Recognition (PQR), Bidirectional Encoder Representations From Transformers (BERT), Generative Adversarial Networks (GAN), Recurrent Neural Network (RNN), and Sequence-To-Sequence (seq-2-seq) model. Also, the paper describes the newest datasets (PubMed, arXiv, SUMPUBMED, Evidence-Based Medicine Summarization, COVID-19 Open Research, BioMed Central, Clinical Context-Aware, Biomedical Relation Extraction Dataset, Semantic Scholar Open Research Corpus, and Prognosis Quality Recognition) and evaluation metrics (Recall-Oriented Understudy for Gisting Evaluation (ROUGE), F1 Metric, Bilingual Evaluation Understudy (BLEU), BERTScore (BS), and Accuracy) used in medical text summarization. [ABSTRACT FROM AUTHOR]

Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design. [ABSTRACT FROM AUTHOR]

In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers architecture, have made it possible to create effective tools for capturing and elaborating news from the Internet. In this regard, this work proposes, for the first time in the literature to the best of the authors' knowledge, a methodology for the application of such techniques in news related to cryptocurrencies and the blockchain, whose quick reading can be deemed as extremely useful to operators in the financial sector. Specifically, cutting-edge solutions in the field of natural language processing were employed to cluster news by topic and summarize the corresponding articles published by different newspapers. The results achieved on 22,282 news articles show the effectiveness of the proposed methodology in most of the cases, with 86.8 % of the examined summaries being considered as coherent and 95.7 % of the corresponding articles correctly aggregated. This methodology was implemented in a freely accessible web application. [ABSTRACT FROM AUTHOR]

The enormous quantity of text makes it challenging for users to obtain the key information and knowledge. Automatic text summarization can alleviate this problem by providing reliable summaries for massive text documents. During the last decade, significant achievements have been made in text summarization. We conduct this survey to explore what research community is focused on, the application scenarios of summarization, the state-of-the-art techniques and methods, and to analyze the challenges and future direction. We summarize that incorporating with natural language processing, previous text summarization research applied knowledge-based methods, graph-based methods, statistical learning methods, and deep learning methods. Applying large language model to text summarization is still in its early stages. By analyzing current research progress, we conclude that understand semantic information and specific domain knowledge is required for text summarization, and the conciseness and readability of the summary should be ensured. The future research opportunity is automatic knowledge summarization, and more research effort is urgently needed to explore. • Significant achievements of automatic text summarization have been made. • We aim to explore the research community of automatic text summarization. • Incorporating with NLP, text summarization applied knowledge-based, graph-based and machine learning methods. • The combination of extractive and abstractive strategies is required, which can ensure the quality of the summary. • The future research opportunity is automatic knowledge summarization. [ABSTRACT FROM AUTHOR]

Cyberbullying poses a digital threat to society. In this survey, we explain what cyberbullying is and its various forms. We focus on social media platforms and instant messaging apps that are susceptible to cyberbullying, discussing how we can identify such behavior in these spaces. Moving on, we conduct a systematic review of publicly available datasets in different languages, exploring techniques for data preprocessing, feature representation, and methodologies used in textual analysis for cyberbullying detection. We specifically look at natural language-based and platform-specific preprocessing methods. We also cover popular feature representation techniques like sentiment analysis, user information, text summarization, symbols, images, and word embedding for detecting cyberbullying. Next, we categorize existing techniques, including machine learning and neural networks, highlighting research gaps. Additionally, we discuss the challenges associated with current datasets and methods. This survey aims to provide early researchers with insights into cyberbullying literature and guide them in exploring potential research directions. • Analysis of prior literature for cyberbullying detection. • Proposes possible future research directions based on identified research challenges. • Identifies the need for high-quality datasets with contextual information. • Develop mitigation systems relevant in real-time. [ABSTRACT FROM AUTHOR]

Text summarization is a widely-researched problem among scholars in the field of natural language processing. Multiple techniques have been proposed to help tackle this problem, yet some of these methodologies may still exhibit limitations such as the requirements for large training datasets, which might not always be possible, but more importantly, the lack of interpretability or transparency of the model. In this paper, we propose using meta-learning algorithm to train a deep learning model for extractive text summarization and then using various explanatory techniques such as SHAP (Shapley, 1953), linear regression (Lederer, 2022), decision trees (Fürnkranz, 2010), and input modification to gain insights into the model's decision making process. To evaluate the effectiveness of our approach, we will compare it to other popular natural language processing models like BERT (Miller, 2019) or XLNET (Yang et al., 2020) using the ROUGE metrics (Lin, 2004). Overall, our proposed approach provides a promising solution to the limitations of existing methods and a framework for improving the explainability of deep learning models in other natural language processing tasks. • Bi-LSTM and Meta-learning produce better results compared to other popular models. • Meta-learning can be utilized to improve performance in data-scarce scenarios. • Use SHAP, regression, decision tree, and text mutation to enhance interpretability. [ABSTRACT FROM AUTHOR]

The scientific research process generally starts with the examination of the state of the art, which may involve a vast number of publications. Automatically summarizing scientific articles would help researchers in their investigation by speeding up the research process. The automatic summarization of scientific articles differs from the summarization of generic texts due to their specific structure and inclusion of citation sentences. Most of the valuable information in scientific articles is presented in tables, figures, and algorithm pseudocode. These elements, however, do not usually appear in a generic text. Therefore, several approaches that consider the particularity of a scientific article structure were proposed to enhance the quality of the generated summary, resulting in ad hoc automatic summarizers. This paper provides a comprehensive study of the state of the art in this field and discusses some future research directions. It particularly presents a review of approaches developed during the last decade, the corpora used, and their evaluation methods. It also discusses their limitations and points out some open problems. The conclusions of this study highlight the prevalence of extractive techniques for the automatic summarization of single monolingual articles using a combination of statistical, natural language processing, and machine learning techniques. The absence of benchmark corpora and gold standard summaries for scientific articles remains the main issue for this task. [ABSTRACT FROM AUTHOR]

When people debate, they want to familiarize themselves with a whole range of arguments about a given topic in order to deepen their knowledge and inspire new claims. However, the amount of differently phrased arguments is humongous, making the process of processing them time-consuming. In spite of many works on using arguments (e.g. counter-argument generation), there is only a few studies on argument aggregation. To address this problem, we propose a new task in argument mining – Argument Extraction, which gathers similar arguments into key points, usually single sentences describing a set of arguments for a given debate topic. Such a short summary of related arguments has been manually created in previous research, while in our research key point generation becomes fully automatic, saving time and cost. As the first step of key point generation we explore existing similarity calculation methods, i.e. Sentence-BERT and MoverScore to investigate their performance. Next, we propose a combination of argument similarity and Maximal Marginal Relevance (MMR) for extracting key phrases to be utilized in our novel task of Argument Extraction. Experimental results show that MoverScore-based MMR outperforms strong baselines covering 72.5% of arguments when eleven or more arguments are extracted. This percentage is almost identical with the cover rate of human-made key points.

Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic text summarization in different languages using various text summarization approaches. The focal centre of this survey paper is to present the research done on text summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of text summarization approaches applied on both Indian and non-Indian languages. [ABSTRACT FROM AUTHOR]

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story. [ABSTRACT FROM AUTHOR]

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level 'local' relation's to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model's modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently. [ABSTRACT FROM AUTHOR]

Abstract Due to the tremendous increment of data on the web, extracting the most important data as a conceptual brief would be valuable for certain users. Therefore, there is a massive enthusiasm concerning the generation of automatic text summary frameworks to constitute abstracts automatically from the text, web, and social network messages associated with their satellite content. This survey highlights, for the first time, how the swarm intelligence (SI) optimization techniques are performed to solve the text summarization task efficiently. Additionally, a convincing justification of why SI, especially Ant Colony Optimization (ACO), has been presented. Unfortunately, three types of text summarization tasks using SI indicate bit utilizing in the literature when contrasted with the other summarization techniques as machine learning and genetic algorithms, in spite of the fact that there are seriously promising outcomes of the SI methods. On the other hand, it has been noticed that the summarization task with multiple types has not been formalized as a multi-objective optimization (MOO) task before, despite that there are many objectives which can be considered. Moreover, the SI was not employed before to support the real-time summary approaches. Thus, a new model has been proposed to be adequate for achieving many objectives and to satisfy the real-time needs. Eventually, this study will enthuse researchers to further consider the various types of SI when solving the summarization tasks, particularly, in the short text summarization (STS) field. [ABSTRACT FROM AUTHOR]

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.

Unlabelled: Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care.Objective: To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs.Materials and Methods: We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure.Results: The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62).Conclusions: The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. [ABSTRACT FROM AUTHOR]

Computational linguistics encompasses a broad range of ideas and research areas, and only a brief introduction is possible here. We chose to include areas in computational linguistics where statisticians can contribute, hoping to provide inspiration to the reader. We describe three main aspects of this discipline-formal languages, information retrieval, and machine learning. These support the overarching goal, which is the representation and analysis of meaning from unstructured text. We then provide an example where text analysis has been applied to unstructured text fields in survey records and conclude with some applications and computational resources. WIREs Comput Stat 2015, 7:258-274. doi: 10.1002/wics.1353 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores. Sentence scores are calculated using their surface-level features, and summaries are created by extracting the highest ranked sentences from the original documents. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, we use features such as term frequency, key phrase (KP), centrality, title similarity and sentence position. The sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine-learning techniques with the help of human-constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries of two newly created Turkish data sets. This paper presents one of the first Turkish summarization systems, and its results are promising. We introduce the usage of KP as a surface-level feature in text summarization, and we show the effectiveness of the centrality feature in text summarization. The effectiveness of the features in Turkish text summarization is also analyzed in detail. [ABSTRACT FROM PUBLISHER]