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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]

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]

In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians' time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5 % of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research. [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]

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]

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students' self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students' learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students' text marking skill and facilitating precision education. [ABSTRACT FROM AUTHOR]

• Statistical feature based extractive approach for multi-document summarization. • Fuzzy logic system is used for sentence scoring. • Cosine similarity measure is used to tackle redundancy from summary. Nowadays abundant amount of information is available on Internet which makes it difficult for the users to locate desired information. Automatic methods are needed to efficiently sieve and scavenge useful information from the Internet. Text summarization is identified and accepted as one of the solutions to find desired contents from one or more documents. The objective of proposed multi-document summarization is to gain good content coverage with information diversity. The proposed statistical feature based model utilizes the fuzzy model to deal with the imprecise and uncertainty of feature weight. Redundancy removal using cosine similarity is presented as enrichment to proposed work. The proposed approach is compared with DUC (Document Understanding Conference) participant systems and other summarization systems such as TexLexAn, ItemSum, Yago Summarizer, MSSF and PatSum using ROUGE measure on dataset DUC 2004. The experimental results show that our proposed work achieves a significant performance improvement over the other summarizers. [ABSTRACT FROM AUTHOR]

The problem of factual incorrectness in machine-generated abstractive summarization has received widespread attention in the past few years. Although large-scale neural models show excellent capability in generating fluent and coherent summaries, they still struggle with factual inconsistency, in which the named entity incorrectness is the most frequent and notable one, especially for the character-based languages, such as Chinese. Since abstractive summaries are mostly generated character by character in Chinese, the problem of hallucinated entities is more severe than that of other word-based languages. In this paper, we propose CC-Gens, a novel approach for Correctness Checking based on a Generative negative sampling strategy. Considering that the problem is due to the uncertain nature of the language generation process, we leverage fine-tuned generative language models, i.e., UniLMv2 and mT5, to generate summaries with incorrect entities, thereby constructing a synthetic binary classification dataset for the factuality discriminative model. We propose three strategies: Entity Sampling, Sequence Sampling, and Cloze Sampling, to generate summaries with incorrect entities. With such strategies, the negative samples are much more similar in nature to the output of the neural summarization model. We then train a BERT-based discriminator to identify factually incorrect machine-generated summaries with entity errors based on these generative negative samples. We further propose a novel PU learning algorithm to improve the performance of our approach by iteratively training the discriminator to select high confident negative samples from the unlabeled model generated summaries to replace the former artificially constructed ones. By generating multiple candidate summaries and selecting the one with the highest factual correctness score among them, our approach can significantly reduce the probability that an output summary contains factual errors. According to a comprehensive evaluation, the CC-Gens we proposed outperforms previous works in identifying faithless summaries as well as providing faithful ones. [Display omitted] • Named entity error ratio in Chinese abstractive summaries is as high as 58.1%. • We propose generative negative sampling strategies for training the discriminator. • We apply PU learning to improve performance by selecting high-confident samples. [ABSTRACT FROM AUTHOR]

Abstract: The entity linking task consists in automatically identifying and linking the entities mentioned in a text to their uniform resource identifiers in a given knowledge base. This task is very challenging due to its natural language ambiguity. However, not all the entities mentioned in the document have the same utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in the document, also known as salient entities (SE), is attracting increasing interest. In this paper, we propose salient entity linking, a novel supervised 2‐step algorithm comprehensively addressing both entity linking and saliency detection. The first step is aimed at identifying a set of candidate entities that are likely to be mentioned in the document. The second step, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on 2 different data sets show that the proposed algorithm outperforms state‐of‐the‐art competitors and is able to detect SE with high accuracy. Furthermore, we used salient entity linking for extractive text summarization. We found that entity saliency can be incorporated into text summarizers to extract salient sentences from text. The resulting summarizers outperform well‐known summarization systems, proving the importance of using the SE information. [ABSTRACT FROM AUTHOR]

The number and size of datasets abiding by the Linked Data paradigm increase every day. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users. Particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we extend our previous work (Ahmed et al., 2019) by proposing a generic multilingual approach that allows verbalization of LS in many languages, i.e., converts LS into understandable natural language text. In this work, we ported our LS verbalization framework into German and Spanish, in addition to English language. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users. Moreover, we devised an experimental neural approach for improving the quality of our generated texts. Our neural approach achieves promising results in terms of BLEU, METEOR and chrF++. [ABSTRACT FROM AUTHOR]

• A comprehensive introduction of rhetorical structure theory (RST). • A snapshot and some statistical data of RST-DT. • An elaboration of the classical and latest RST parsing methods. • Applications of RST, especially in expert and intelligent systems. • Comparisons with other discourse structure theories. Rhetorical structure theory (RST) is a significant theory about discourse organization. With an increasing number of research interests focus on RST, many novel parsing approaches have been proposed and motivated many brand new applications, such as chatbots and other expert and intelligent systems. However, the work on RST dates back many years and there remains a lack of comprehensive literature reviews. The aim of this study is therefore to provide a comprehensive overview of RST, parsing methods and applications. In this paper, we first give a detailed introduction to RST. Then the commonly used discourse treebank: RST-DT is elaborated. We propose a new taxonomy to divide the RST parsing methods into different categories. With a focus on the classical and latest methods that have recently been developed, we review the pros and cons of these approaches, along with the performance analysis of them. We then summarize the applications of RST across various domains. Moreover, we present a comparative study of RST with other discourse structure theories. Finally, we discuss some implications of our findings and outline future research directions in this challenging and fast-growing field. [ABSTRACT FROM AUTHOR]