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Objective: The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain., Materials and Methods: MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation., Results: Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation., Discussion: This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization., Conclusion: Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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]

A long-term objective of artificial intelligence is to design an abstractive text summarization (ATS) system that can produce condensed, adequate, and realistic summaries for the source documents. Deep learning approaches have contributed significantly to recent advancements in ATS, taking the state-of-the-art to new heights. Despite the considerable success of earlier approaches, producing high-quality and more human-like abstractive summaries continues to be a challenging task in reality. In this paper, we present an adversarial framework for abstractive text summarization, in which a generative model G and a proposed deep discriminative model D are trained in an adversarial manner. The goal of the generator is to generate summaries that are hard to differentiate from real summaries, whereas the discriminator's role is to estimate the probability that a summary came from the training data rather than the generator to direct the generative model's training. Experimental results on a benchmark CNN/Daily Mail dataset demonstrate that the proposed model achieves Rouge-1 and Rouge-L scores of 41.58 and 38.96 respectively which are better than the ones reported by various other methods (e.g. the base paper [1] achieved Rouge-1 and Rouge-L score of 39.92 and 36.71, while one of the recent works ACGT [2] achieved Rouge-1 score of 40.49 and Rouge-L score of 37.41). The manuscript's unique characteristic is qualitative evaluation, which, along with quantitative evaluation, shows that the proposed model is superior. [ABSTRACT FROM AUTHOR]

Medical imaging description and disease diagnosis are vitally important yet time-consuming. Automated diagnosis report generation (DRG) from medical imaging description can reduce clinicians' workload and improve their routine efficiency. To address this natural language generation task, fine-tuning a pre-trained large language model (LLM) is cost-effective and indispensable, and its success has been witnessed in many downstream applications. However, semantic inconsistency of sentence embeddings has been massively observed from undesirable repetitions or unnaturalness in text generation. To address the underlying issue of anisotropic distribution of token representation, in this study, a contrastive learning penalized cross-entropy (CLpCE) objective function is implemented to enhance the semantic consistency and accuracy of token representation by guiding the fine-tuning procedure towards a specific task. Furthermore, to improve the diversity of token generation in text summarization and to prevent sampling from unreliable tail of token distributions, a diversity contrastive search (DCS) decoding method is designed for restricting the report generation derived from a probable candidate set with maintained semantic coherence. Furthermore, a novel metric named the maximum of token repetition ratio (maxTRR) is proposed to estimate the token diversity and to help determine the candidate output. Based on the LLM of a generative pre-trained Transformer 2 (GPT-2) of Chinese version, the proposed CLpCE with DCS (CLpCEwDCS) decoding framework is validated on 30,000 desensitized text samples from the "Medical Imaging Diagnosis Report Generation" track of 2023 Global Artificial Intelligence Technology Innovation Competition. Using four kinds of metrics evaluated from n-gram word matching, semantic relevance, and content similarity as well as the maxTRR metric extensive experiments reveal that the proposed framework effectively maintains semantic coherence and accuracy (BLEU-1, 0.4937; BLEU-2, 0.4107; BLEU-3, 0.3461; BLEU-4, 0.2933; METEOR, 0.2612; ROUGE, 0.5182; CIDER, 1.4339) and improves text generation diversity and naturalness (maxTRR, 0.12). The phenomenon of dull or repetitive text generation is common when fine-tuning pre-trained LLMs for natural language processing applications. This study might shed some light on relieving this issue by developing comprehensive strategies to enhance semantic coherence, accuracy and diversity of sentence embeddings. [ABSTRACT FROM AUTHOR]

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students' weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3. [ABSTRACT FROM AUTHOR]

The challenges faced by the massive increase in scientific publications draw parallels to the Larsen effect, where an amplified sound loop leads to escalating noise. This phenomenon has resulted in information overload, making it difficult for researchers to stay updated and identify significant findings. To address this, knowledge synthesis techniques are recommended. These methods help synthesize and visualize large bodies of literature, aiding researchers in navigating the expanding information landscape. Furthermore, artificial intelligence (AI) and natural language processing tools, such as text summarization, offer innovative solutions for managing information overload. However, the overuse of AI in producing scientific literature raises concerns about the quality and integrity of research. This manuscript highlights the need for balanced use of AI tools and collaborative efforts to maintain high‐quality scientific output while leveraging the benefits of extensive research. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decisionmaking process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates secondorder effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Future Internet, focusing on the state of Future Internet Technology in Italy. The issue covers a range of topics, including anonymous service delivery, virtual environments for furnishings sales, Italian-language sequence-to-sequence models for text summarization, knowledge scaffolding in Wikipedia, the integration of Operation Technology and Information Technology networking in Industry 4.0, a digital platform for healthcare operators, self-admitted technical debt in blockchain initiatives, unified analytics for smart cities, the integration of natural language processing and artificial intelligence in e-learning, and a system for attack detection in network architectures. The authors provide detailed explanations and evaluations of each topic, highlighting their potential benefits and applications. [Extracted from the article]

Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre‐trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common‐sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research. This article is categorized under:Fundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data. [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]

This review paper delves into the emerging realm of YouTube video summarization utilizing Natural Language Processing (NLP) techniques, a critical area of research with increasing prominence in our multimedia-rich digital age. The paper commences with a broad overview of the field, elaborating on the need for automated video summarization tools to navigate and condense the massive, ever-growing sea of YouTube content. Further, we systematically scrutinize the role and implementation of NLP methods in extracting meaningful textual data from videos, focusing on video transcripts, closed captions, user comments, and associated metadata. Subsequent sections dissect seminal and recent works, studying various NLP techniques such as text summarization, sentiment analysis, topic modeling, and deep learning architectures employed in this context. The paper also focuses on the various metrics used for evaluation and shows datasets generally used to assess the performance of these summarization systems. Finally, we identify current challenges and potential future directions for research in the area, acknowledging the evolving landscape of online video platforms and AI technologies. This review aims to provide researchers and practitioners with an encompassing perspective on the pivotal role of NLP in enabling more efficient, accurate, and intuitive navigation of YouTube content ultimately shaping our digital consumption experiences. [ABSTRACT FROM AUTHOR]

Over the past few years, the explainable artificial intelligence (XAI) model receives a broad desire for investigation. The natural language processing (NLP) commune is reaching the fundamental change too – constructing a set of paradigms, which describe the preference on a few chief jobs devoid of influencing the execution. Abstractive Text Summarization (ATS) remains the job of building summary sentences by fusing factualities out of disparate source sentences and compressing them into a smaller portrayal when sustaining data content and comprehensive sense. This remains extremely arduous and long-drawn-out for people to physically summarize huge text documents. This study proffers Ontology-based Knowledge Aware Multi-focus Conditional Generative Adversarial Network (OKAM-CGAN) for novel documents. This could build novel sentences by analyzing many finer pieces than sentences, especially, semantic phrases. The proffered OKAM-CGAN comprises 3 prime portions – ontology aware knowledge-based document representation module, multitask and multi-focus learning unit, and an adversarial network unit. Experiential assessment is performed by correlating with advanced methodologies like RNN-W, CopyNet, GCU, Seq2Seq, and KESG concerning ROUGE scores. Consequently, it is observed that the proffered OKAM-CGAN attains 42.1% of ROUGE-L, 40% of accuracy, 45%of precision, and 53% of recall for the CNN/Daily Mail database and 45% of ROUGE-L, 4% of accuracy, 54% of precision, and 57% of recall for the Edmunds database. [ABSTRACT FROM AUTHOR]

Artificial Intelligence in the field of Law has introduced a lot of innovations to aid legal experts, and one of those are summarizing systems. Text summarization is the process of condensing text into a compact summary highlighting the important parts of the original text. As years pass by, numerous text summarization systems are being developed, and improvements keep getting made. These advancements have been helping humans in easing labor work with summarizing lengthy documents. With this, the researchers would like to propose a tool that will summarize legal documents, specifically cases obtained from the Philippine Supreme Court website. This includes extracting meanings and analyzation of words, and similarity of sentences from the original legal document obtained from the website of the Philippine Supreme Court. The tool will take the content of the original document, analyze the words, structure, and sentences according to context, and finally produce the summary. The proposed tool will be called SUMMIT, from the words "Summarize It". The method to be used to summarize the documents using the tool is called Latent Semantic Analysis (LSA). The researchers will use Python as their programming language in developing the summarizer tool. [ABSTRACT FROM AUTHOR]

This article discusses the role of Artificial Intelligence (AI) in supporting the production and distribution of high-quality news in the face of challenges such as misinformation and declining trust in institutions. The article explores how AI, including Machine Learning and generative AI, can be used in various stages of news production and dissemination. It also highlights the importance of considering factors beyond technology, such as organizational and economic contexts, in order to successfully harness the potential of AI in the media sector. The article presents several papers that examine different aspects of AI's impact on news, including its influence on audience behavior, financial reporting, local journalism, news recommendation systems, and misinformation correction. The authors emphasize the need for ethical considerations and collaboration among stakeholders to ensure the benefits of AI in supporting quality media production. [Extracted from the article]

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]

The exponential growth of digital text data has made text summarization an essential tool in various natural language processing applications. Text compaction, a subset of text summarization, is a process that aims to generate concise and coherent summaries while preserving the main information from the source text. In this research paper, we explore different techniques used in text compaction, their applications in the real world, and the potential for future development and improvements. [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]

Generative ability is a crucial need for artificial intelligence applications, such as chatbots, virtual assistants, machine translation systems etc. In recent years, the transformer-based neural architectures gave a huge boost to generate human-like English texts. In our research we did experiments to create pre-trained generative transformer models for Hungarian language and fine-tune them for multiple types of natural language processing tasks. In our focus, multilingual models were trained. We have pre-trained a multilingual BART, then fine-tuned it to various NLP tasks, such as text classification, abstractive summarization. In our experiments, we focused on transfer learning techniques to increase the performance. Furthermore, a M2M100 multilingual model was fine-tuned for a 12-lingual Hungarian- Centric machine translation. Last but not least, a Marian NMT based machine translation system was also built from scratch for the 12-lingual Hungarian-Centric machine translation task. In our results, using the cross-lingual transfer method we could achieve higher performance in all of our tasks. In our machine translation experiment, using our fine-tuned M2M100 model we could outperform the Google Translate, Microsoft Translator and eTranslation. [ABSTRACT FROM AUTHOR]

Text summarization is an important research topic in information technology because of the large volume of texts and the large amount of data on the Internet and social media. As a result, summarizing the text has gained significant importance, requiring finding highly efficient ways to extract knowledge in various fields. Thus, there was a need for methods of summarizing texts for one document or multiple documents. Furthermore, the summarization methods aim to obtain the main content of the set of documents simultaneously to reduce redundant information. This paper proposes an efficient method to summarize texts that depends on the word association algorithm to separate and merge sentences after summarizing them. As well as the use of data mining technology in redistributing information according to the (K-Mean) algorithm and the use of (Term Frequency Inverse Document Frequency TF-IDF) technology for measuring the properties of summarized texts. The experimental results found that the summarization ratios are good by deleting unimportant words. Also, extracting characteristics for texts was useful in grouping similartexts into clusters, which makes this method possible to be combined with other methods in artificial intelligence, such as fuzzy logic or evolutionary algorithms, in increasing summarization rates and accelerating cluster operations. [ABSTRACT FROM AUTHOR]

The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods. [ABSTRACT FROM AUTHOR]

With the rise in the amount of textual data over the internet, the demand for summarizing it in a short, readable, easy-to-understand form has increased. Much of the research is being carried out to improve the efficiency of these text summarization systems. In the past, extractive text summarization was mainly carried out through human-crafted features which were unable to learn the semantic information from the text. Therefore, in an attempt to improve the quality of summary, we have designed a neural network-based completely data-driven model for extractive single-document summarization of text which we have termed as WL-AttenSumm. Our proposed model implements a Word-level Attention mechanism that focuses more on the important parts in the input sequence so relevant semantic features are captured at the word-level that helps in selecting significant sentences for the summary. Another advantage of this model is that it can extract syntactic and semantic relationships from the text by using a Convolutional Bi-GRU (Bi-directional Gated Recurrent Unit) network. We have trained our proposed model on the combined CNN/Daily Mail corpus and evaluated on the Daily Mail, combined CNN/Daily Mail, and DUC 2002 test dataset for single document summarization and obtained better results as compared to the state-of-the-art baseline approaches in terms of ROUGE metrics. For the summary length limited to 75 words, our attention-based approach generates ROUGE recall scores for R-1, R-2, R-L measures as 32.8%, 11.0%, 27.5% with Daily Mail corpus and 55.9%, 24.8%, 53.9% with DUC 2002 dataset, respectively. Experiments performed with the joint CNN/Daily dataset yield full-length ROUGE F1 scores as 42.9%, 19.7%, 39.3%. Therefore, our deep learning-based summarization framework achieves competitive performance. [ABSTRACT FROM AUTHOR]

In the era of big data, information overload problems are becoming increasingly prominent. It is challenging for machines to understand, compress and filter massive text information through the use of artificial intelligence technology. The emergence of automatic text summarization mainly aims at solving the problem of information overload, and it can be divided into two types: extractive and abstractive. The former finds some key sentences or phrases from the original text and combines them into a summarization; the latter needs a computer to understand the content of the original text and then uses the readable language for the human to summarize the key information of the original text. This paper presents a two-stage optimization method for automatic text summarization that combines abstractive summarization and extractive summarization. First, a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initial summarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcement learning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2, and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset. [ABSTRACT FROM AUTHOR]

Although pretrained language models achieve high performance on various natural language processing tasks, they still require further improvements in the sentence embedding task. Many studies have improved performance in this task using pre-trained language models and contrastive learning, but these approaches are limited because they are based on naive average pooling and CLS tokens. Therefore, we propose an advanced sentence-embedding method based on weighted pooling that considers token importance. Specifically, the token importance is calculated by combining an explainable artificial-intelligence module with a text summarization model, and the final sentence embedding is derived through weighted pooling token embedding and token importance. Thus, we derive a sentence embedding that considers both the local information of the token embedding and the global information of the entire sentence. Experimental results reveal that our proposed sentence embedding outperforms other models on both text similarity tasks and text classification. Moreover, the proposed method's robustness is verified through the results of an ablation study. [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]

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations. [ABSTRACT FROM AUTHOR]

Semantic applications typically exploit structures such as dependency parse trees, phrase-chunking, semantic role labeling or open information extraction. In this paper, we introduce a novel application of Open IE as an intermediate layer for text summarization. Text summarization is an important method for providing relevant information in large digital libraries. Open IE is referred to the process of extracting machine-understandable structural propositions from text. We use these propositions as a building block to shorten the sentence and generate a summary of the text. The proposed system offers a new form of summarization that is able to break the structure of the sentence and extract the most significant sub-sentential elements. Other advantages include the ability to identify and eliminate less important sections of the sentence (such as adverbs, adjectives, appositions or dependent clauses), or duplicate pieces of sentences which in turn opens up the space for entering more sentences in the summary to enhance the coverage and coherency of it. The proposed system is localized for Persian language; however, it can be adopted to other languages. Experiments performed on a standard data set “Pasokh” with a standard comparison tool showed promising results for the proposed approach. We used summaries produced by the system in a real-world application in the virtual library of Shahid Beheshti University and received good feedbacks from users. [ABSTRACT FROM AUTHOR]

Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task. [ABSTRACT FROM AUTHOR]

Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is still a lack of large-scale high quality datasets for this technique. In this paper, we proposed a novel deep learning method to identify high quality document--summary pairs for building a large-scale pairs dataset. Concretely, a long short-term memory (LSTM)-based model was designed to measure the quality of document--summary pairs. In order to leverage information across all parts of each document, we further proposed an improved LSTM-based model by removing the forget gate in the LSTM unit. Experiments conducted on the training set and the test set built upon Sina Weibo (a Chinese microblog website similar to Twitter) showed that the LSTM-based models significantly outperformed baseline models with regard to the area under receiver operating characteristic curve (AUC) value. [ABSTRACT FROM AUTHOR]

As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed. [ABSTRACT FROM AUTHOR]

Text summarization is either extractive or abstractive. Extractive summarization is to select the most salient pieces of information (words, phrases, and/or sentences) from a source document without adding any external information. Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source. In this case, external text can be inserted into the generated summary. Because of the complexity of the abstractive approach, the vast majority of work in text summarization has adopted an extractive approach. In this work, we focus on concepts fusion and generalization, i.e. where different concepts appearing in a sentence can be replaced by one concept which covers the meanings of all of them. This is one operation that can be used as part of an abstractive text summarization system. The main goal of this contribution is to enrich the research efforts on abstractive text summarization with a novel approach that allows the generalization of sentences using semantic resources. This work should be useful in intelligent systems more generally since it introduces a means to shorten sentences by producing more general (hence abstractions of the) sentences. It could be used, for instance, to display shorter texts in applications for mobile devices. It should also improve the quality of the generated text summaries by mentioning key (general) concepts. One can think of using the approach in reasoning systems where different concepts appearing in the same context are related to one another with the aim of finding a more general representation of the concepts. This could be in the context of Goal Formulation, expert systems, scenario recognition, and cognitive reasoning more generally. We present our methodology for the generalization and fusion of concepts that appear in sentences. This is achieved through (1) the detection and extraction of what we define as generalizable sentences and (2) the generation and reduction of the space of generalization versions. We introduce two approaches we have designed to select the best sentences from the space of generalization versions. Using four NLTK 1 1 The Natural Language Tool Kit ( http://nltk.org/ ) corpora, the first approach estimates the “acceptability” of a given generalization version. The second approach is Machine Learning-based and uses contextual and specific features. The recall, precision and F1-score measures resulting from the evaluation of the concept generalization and fusion approach are presented. [ABSTRACT FROM AUTHOR]

Text classification (TC) can be described as the act of assigning text documents to predefined classes or categories. Its necessity comes from the large amount of electronic documents on the web. The classification accuracy is affected by the content of documents and the classification technique being used. Automatic text summarization is based on identifying the set of sentences that are most important for the overall understanding of document(s). The need for text summarization comes from the large amount of electronic documents and the need for saving processing time. In this research, an automatic text summarizer has been used to summarize documents. Two classification methods have been used to classify Arabic documents before and after applying the summarization, then the classification accuracy of classifying the full documents and summarized documents have been compared. Classification accuracy resulted from classifying full documents is close to that resulted from classifying summarized documents. Nevertheless, memory space required and run time for classifying summarized documents are less than the memory and time needed for classifying full documents. [ABSTRACT FROM AUTHOR]

Abstract: The rises of the life index quality together with the medical technology improvement lead to a longer life expectancy. Thus a better health care program, especially for elderly, is needed. The common health problems facing those senior citizens are changed from acute diseases to chronic diseases, such as diabetes, hypertension, etc. Along with these changes, medical tourism is becoming the trend of the future. In this paper, we propose a decision support systems, the Ubiquitous Context-aware Healthcare Service System (UCHS), which uses micro sensors integrate RFID to sense user’s life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), and temperature and light. The UCHS is composted of Situation-Aware Medical Tourism Service Search Subsystem (SAMTS3), Healthy-life Map Guiding Subsystem (HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user. The UCHS built upon an integrated service platform in which medical experts’ knowledge and all position and negative influence of the proposed therapy are inferred by using semantic network. [ABSTRACT FROM AUTHOR]