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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]

Automatic detection of prerequisite relations between concepts in education has always been a challenging AI task for researchers. Identification of prerequisite relations enables students to study new subjects more effectively and systematically, while allowing instructors to better tailor their learning materials to students' needs. However, to accurately detect these relations, the AI system must understand the context and meaning behind each concept and how it relates to other concepts in the domain. This requires a deep understanding of the educational curriculum and the ability to analyze large amounts of text and data. Large language models (LLMs) are a recent innovation in AI. LLMs have the capability to understand and generate human-like text since they are trained on a vast amount of text from the internet, books, articles, and more. LLMs can also be fine-tuned to specialize in tasks such as document summarization, question answering, or detecting user sentiment in reviews. Fine-tuning is done with a smaller, task-specific dataset. In this work, we introduce strategies for fine-tuning LLMs to improve their capability to detect prerequisite relations between educational concepts. To the best of our knowledge, this is the first work that utilizes fine-tuned LLMs for prerequisite detection. Our evaluation results demonstrate that fine-tuned LLMs are effective models for prerequisite detection. Our fine-tuning process also facilitates the generation of explanations to shed light on the reasoning behind prerequisite relations. Datasets we generated and used in fine-tuning are made public for the research community. We hope our contributions can aid in organizing and presenting knowledge in education and serve as a foundation for future research in the field. [ABSTRACT FROM AUTHOR]

The object of the research is the task of automatic abstracting of natural language texts. The importance of these tasks is determined by the existing problem of creating essays that would adequately reflect the content of the original text and highlight key information. This task requires the ability of models to deeply analyze the context of the text, which complicates the abstracting process. Results are presented that demonstrate the effectiveness of using generative models based on neural networks, text semantic analysis methods, and deep learning for automatic creation of abstracts. The use of models showed a high level of adequacy and informativeness of abstracts. GPT (Generative Pre-trained Transformer) generates text that looks like it was written by a human, which makes it useful for automatic essay generation. For example, the GPT model generates abbreviated summaries based on input text, while the BERT model is used for summarizing texts in many areas, including search engines and natural language processing. This allows for short but informative abstracts that retain the essential content of the original and provides the ability to produce high-quality abstracts that can be used for abstracting web pages, emails, social media, and other content. Compared to traditional abstracting methods, artificial intelligence provides such advantages as greater accuracy, informativeness and the ability to process large volumes of text more efficiently, which facilitates access to information and improves productivity in text processing. Automatic abstracting of texts using artificial intelligence models allows to significantly reduce the time required for the analysis of large volumes of textual information. This is especially important in today’s information environment, where the amount of available data is constantly growing. The use of these models promotes efficient use of resources and increases overall productivity in a variety of fields, including scientific research, education, business and media [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]

Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain. [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]

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes. [ABSTRACT FROM AUTHOR]

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting. This survey aims to provide a comprehensive review of existing techniques, evaluation methodologies, and practical applications of automatic text summarization. [ABSTRACT FROM AUTHOR]

Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting. [ABSTRACT FROM AUTHOR]

Peer reviews form an essential part of scientific communication. Scholarly peer review is probably the most accepted way to evaluate research papers by involving multiple experts to review the concerned research independently. Usually, the area chair, the program chair, or the editor takes a call weighing the reviewer's judgments. It communicates the decision to the author via writing a meta-review by summarizing the review comments. With the exponential rise in research paper submissions and the corresponding rise in the reviewer pool, it becomes stressful for the chairs/editors to manage conflicts, arrive at a consensus, and also write an informative meta-review. Here in this work, we propose a novel deep neural network-based approach for generating meta-reviews in an unsupervised fashion. To generate consistent meta-reviews, we use a generic template where the task is like to slot-fill the template with the generated meta-review text. We consider the setting where only peer reviews with no summaries or meta-reviews are provided and propose an end-to-end neural network model to perform unsupervised opinion-based abstractive summarization. We first use an aspect-based sentiment analysis model, which classifies the review sentences with the corresponding aspects (e.g., novelty, substance, soundness, etc.) and sentiment. We then extract opinion phrases from reviews for the corresponding aspect and sentiment labels. Next, we train a transformer model to reconstruct the original reviews from these extraction. Finally, we filter the selected opinions according to their aspect and/or sentiment at the time of summarization. The selected opinions of each aspect are used as input to the trained Transformer model, which uses them to construct an opinion summary. The idea is to give a concise meta-review that maximizes information coverage by focusing on aspects and sentiment present in the review, coherence, readability, and redundancy. We evaluate our model on the human written template-based meta-reviews to show that our framework outperforms competitive baselines. We believe that the template-based meta-review generation focusing on aspect and sentiment will help the editor/chair in decision-making and assist the meta-reviewer in writing better and more informative meta-reviews. We make our codes available at https://github.com/sandeep82945/Unsupervised-meta-review-generation. [ABSTRACT FROM AUTHOR]

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions, and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG. [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]

In this paper, we present a rigorous mathematical examination of generative pre-trained transformer (GPT) models and their autoregressive self-supervised learning mechanisms. We begin by defining natural language space and knowledge space, which are two key concepts for understanding the dimensionality reduction process in GPT-based large language models (LLMs). By exploring projection functions and their inverses, we establish a framework for analyzing the language generation capabilities of these models. We then investigate the GPT representation space, examining its implications for the models' approximation properties. Finally, we discuss the limitations and challenges of GPT models and their learning mechanisms, considering trade-offs between complexity and generalization, as well as the implications of incomplete inverse projection functions. Our findings demonstrate that GPT models possess the capability to encode knowledge into low-dimensional vectors through their autoregressive self-supervised learning mechanism. This comprehensive analysis provides a solid mathematical foundation for future advancements in GPT-based LLMs, promising advancements in natural language processing tasks such as language translation, text summarization, and question answering due to improved understanding and optimization of model training and performance. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) involves estimating the salience of information and creating coherent summaries that include all relevant and important information from the original text. Extensive research has been carried out on ATS since 1958, gradually evolving from simple to advanced techniques, including machine learning-based, neural network-based, and deep learning-based techniques. Progress has been made in both extractive and abstractive methods throughout this development. Despite numerous surveys on ATS, there remains a notable absence of a comprehensive literature review encompassing the latest advancements in deep learning techniques for text summarization. Therefore, this paper provides a Systematic Literature Review (SLR) of deep learning-based text summarization in both types (extractive and abstractive) between 2014 and 2023. To the best of our knowledge, this is the first SLR that offers a comprehensive overview of extractive and abstractive text summarization techniques based on Deep Learning models. According to the defined inclusion and exclusion criteria, 73 deep learning-based text summarization studies are selected for further investigation. The structure of the review is organized as follows. Firstly, it identifies and examines the deep learning models employed in both extractive and abstractive text summarization. Then, the input text's representation methods are identified and discussed clearly. Next, training strategies used in supervised extractive summarization techniques are identified. Furthermore, mechanisms that improve the abstractive summarization process are identified. Additionally, the most commonly used datasets and their advantages and disadvantages are discussed. The most commonly used evaluation metrics are also identified. Finally, the challenges and possible solutions to guide future research in the field are discussed. • Analyze the latest deep learning-based text summarization techniques. • Introduce a new taxonomy of deep learning models for text summarization. • Identify methods for representing input text clearly. • Identify training strategies for supervised extractive summarization techniques. • Identify the mechanisms used in abstractive summarization techniques. [ABSTRACT FROM AUTHOR]

Due to the exponential growth in the number of documents on the Web, accessing the salient information relevant to a user need is gaining importance, which increases the popularity of text summarization. Recent progress in deep learning shifted the research in text summarization from extractive methods towards more abstractive approaches. The research and the available resources remain mostly limited to the English language, which prevents progress in other languages. There is need in low-resourced languages for gathering large-scale resources suitable for such tasks. In this study, we release two large-scale datasets (TR-News and HU-News) that can serve as benchmarks in the abstractive summarization task for Turkish and Hungarian. The datasets are primarily compiled for text summarization, but are also suitable for other tasks such as topic classification, title generation, and key phrase extraction. Morphology is important for these agglutinative languages since meaning is carried mostly within the morphemes of the words. We utilize these morphological properties for tokenization to retain the semantic information and reduce the vocabulary sparsity introduced by the agglutinative nature of these languages. Using the datasets compiled, we propose linguistically-oriented tokenization methods (SeparateSuffix and CombinedSuffix) and evaluate them on the state-of-the-art abstractive summarization models. The SeparateSuffix method achieves the highest ROUGE-1 score on the TR-News dataset and provides promising results on the HU-News dataset. In another experiment, we show that the multilingual cased BERT model outperforms monolingual BERT models for both languages and reaches the highest ROUGE-1 score on the HU-News dataset. Lastly, we provide qualitative analysis of the generated summaries on the TR-News dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization. [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 rapid development of textual data requires an automated text summarization system to obtain shortened versions of documents quickly and accurately. This paper investigates the performances of BertSum and Pointer Generator Network (PGN) on the IndoSum corpus containing Indonesian news articles. We compare these methods to NeuralSum, which is claimed to outperform other methods when working with the IndoSum dataset. In our experiment, BertSum with Indonesian's pre-trained model outperformed NeuralSum in extractive summarization. NeuralSum, on the other hand, tends to select the leading sentences as a summary and occasionally produces a blank summary. Meanwhile, PGN effectively prevents word repetition by using a coverage mechanism, although the summary results are sometimes out of context. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) is an essential field in natural language processing that attempts to condense large text documents so that users can assimilate information quickly. It finds uses in medical document summarization, review generation, and opinion mining. This work investigated an unsupervised extractive summarization approach that combined clustering with topic modeling to reduce topic bias. Latent Dirichlet Allocation was used for topic modeling, while K-Medoids clustering was employed for summary generation. The approach was evaluated on three datasets—Wikihow, CNN/DailyMail, and the DUC2002 Corpus. The Recall Oriented-Understudy for Gisting Evaluation (ROUGE) metrics were used for comparative analysis against recently reported techniques, specifically ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). The suggested framework offered scores of 34.80%, 9.13%, and 32.30% on the Wikihow Dataset, 43.90%, 19.01%, and 41.50% on the CNN/DailyMail Dataset, and 49.35%, 31.53%, and 41.72% on the DUC2002 Corpus (R-1, R-2, R-L respectively). These reported metrics are found to be superior when compared to similar recent works. Further, execution time of the proposed method was also recorded and compared with counterparts, which established its superior speed. Based on these promising outcomes, it was concluded that an unsupervised extractive summarization approach with greater subtopic focus significantly improves over generic topic modeling semantic and deep learning approaches. [ABSTRACT FROM AUTHOR]