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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]

Extractive summarization has recently gained significant attention as a classification problem at the sentence level. Most current summarization methods rely on only one way of representing sentences in a document (i.e., extracted features, word embeddings, BERT embeddings). However, classification performance and summary generation quality will be improved if we combine two ways of representing sentences. This paper presents a novel extractive text summarization method based on word embeddings and statistical features of a single document. Each sentence is encoded using a Convolutional Neural Network (CNN) and a Feed-Forward Neural Network (FFNN) based on word embeddings and statistical features. CNN and FFNN outputs are concatenated to classify the sentence using a Multilayer Perceptron (MLP). In addition, hybrid model parameters are optimized by the KerasTuner optimization technique to determine the most efficient hybrid model. The proposed method was evaluated on the standard Newsroom dataset. Experiments show that the proposed method effectively captures the document's semantic and statistical information and outperforms deep learning, machine learning, and state-of-the-art approaches with scores of 78.64, 74.05, and 72.08 for ROUGE-1 ROUGE-2, and ROUGE-L, respectively. [ABSTRACT FROM AUTHOR]