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Summary: Natural language processing (NLP) has undergone a significant transformation, evolving from manually crafted rules to powerful deep learning techniques such as transformers. These advancements have revolutionized various domains including summarization, question answering, and more. Statistical models like hidden Markov models (HMMs) and supervised learning have played crucial roles in laying the foundation for this progress. Recent breakthroughs in transfer learning and the emergence of large‐scale models like BERT and GPT have further pushed the boundaries of NLP research. However, news summarization remains a challenging task in NLP, often resulting in factual inaccuracies or the loss of the article's essence. In this study, we propose a novel approach to news summarization utilizing a fine‐tuned Transformer architecture pre‐trained on Google's mt‐small tokenizer. Our model demonstrates significant performance improvements over previous methods on the Inshorts English News dataset, achieving a 6% enhancement in the ROUGE‐1 score and reducing training loss by 50%. This breakthrough facilitates the generation of reliable and concise news summaries, thereby enhancing information accessibility and user experience. Additionally, we conduct a comprehensive evaluation of our model's performance using popular metrics such as ROUGE scores, with our proposed model achieving ROUGE‐1: 54.6130, ROUGE‐2: 31.1543, ROUGE‐L: 50.7709, and ROUGE‐LSum: 50.7907. Furthermore, we observe a substantial reduction in training and validation losses, underscoring the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Amharic is one of the under‐resourced languages, making news headline generation particularly challenging due to the scarcity of high‐quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine‐tuned the small check point of the T5v1.1 model (t5‐small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine‐tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF‐IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge‐L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine‐tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule‐based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge‐L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine‐tuned T5v1.1 model and the result of previous studies report on abstractive‐based text summarization for Amharic language, which had a 0.27 Rouge‐L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine‐tuning other available pretrained models for text summarization. [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]

Question‐driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query‐based and question‐driven summarization may not produce reliable summaries nor contain relevant information if they do not take advantage of extractive and abstractive summarization mechanisms to improve performance. In this article, we propose a novel extractive and abstractive hybrid framework designed for question‐driven automatic text summarization. The framework consists of complimentary modules that work together to generate an effective summary: (1) discovering appropriate non‐redundant sentences as plausible answers using an open‐domain multi‐hop question answering system based on a convolutional neural network, multi‐head attention mechanism and reasoning process; and (2) a novel paraphrasing generative adversarial network model based on transformers rewrites the extracted sentences in an abstractive setup. Experiments show this framework results in more reliable abstractive summary than competing methods. We have performed extensive experiments on public datasets, and the results show our model can outperform many question‐driven and query‐based baseline methods (an R1, R2, RL increase of 6%–7% for over the next highest baseline). [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]

Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases. [ABSTRACT FROM AUTHOR]

Query‐focused multi‐document summarization (Qf‐MDS) is a sub‐task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf‐MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source‐to‐summary compression ratio. In this work, we propose a semantic diversity feature based query‐focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query‐relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge‐based vectorial representation to extract effective sentence importance and query‐relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query‐focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state‐of‐the‐art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE‐1, ROUGE‐2, and ROUGE‐SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE‐1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents. [ABSTRACT FROM AUTHOR]

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]

Capturing valuable product/service improvement ideas is helpful for the development of new features. However, the existing methods for capturing such improvement ideas have the disadvantages of high cost, long time lag, information overload, and difficulty in getting a response. We propose an innovative framework based on lead user theory for capturing product/service improvement ideas from user‐generated content on social media (henceforth called "chatter"). To identify the chatter containing improvement ideas, we design a machine‐learning‐based imbalanced classification model. Additionally, we use text summarization technology to get a rough sense of improvement ideas from the selected chatter. We validate the proposed framework by a case study in the automotive industry. The results demonstrate that the ideas extracted by our framework are breakthrough innovative, useful, feasible, and adoptable. [ABSTRACT FROM AUTHOR]

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

During emergency situations, a quick and concise summary from the deluge of messages improves "situation awareness" and enables informed decisions. However, this is challenging due to the volume, variety, and veracity of information. Grounded in the Situation Awareness Theory, this study presents a streamlined protocol to process millions of social media messages and render them in an easily comprehensible format for various stakeholders to gain deeper insights. Specifically, using a design science approach, we develop a set of artifacts using incremental hierarchical clustering and enhanced text summarization algorithm to produce informative summaries under crisis situations. We implemented our protocol on 2.5 and 3 million tweets collected during the two major hurricanes in 2017 and 2018. The results show that our protocol can derive critical information not captured by Twitter's search tools and mainstream news media and significantly improve on accuracy and efficiency when compared to other contemporary tools. [ABSTRACT FROM AUTHOR]

PROBLEM: Text summarization is widely used to extract relevant information from documents. The extensive unstructured data are generated in various domains, developing a concise and coherent form of information has become an inevitable and assertive problem among the research community. Automatic text summarization plays a crucial role in acquiring potent knowledge effectively and efficiently for each domain of information. Medical data contains crucial information regarding human diseases and their symptoms. The proposed method will benefit the research community and health experts as it saves a lot of time and resources to compute the patients' summary during diagnosis. APPROACH: In this research, an unsupervised approach is proposed for extractive summarization, based on semantic similarity and keyword‐phrase extraction. Merging Concept Map and the RAKE method, a generic summary is computed based on threshold values. Both single‐document and multi‐document summarization can be accomplished with the approach. RESULTS: To evaluate the proposed unsupervised approach, various biomedical transcripts of neuro‐science, general medicine, gastroenterology, orthopaedic and radiology domain are used. MT Sample Dataset is used to collect 1040 different transcripts. Using the proposed approach, an average ROUGE score of 0.77 for single‐document summarization; however, for generic summary, an average ROUGE is 0.72. The proposed technique is validated for the previous corpus of BioMed articles, and results are better with state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

Exponential increase in the amount of textual data made available on the Internet results in new challenges in terms of accessing information accurately and quickly. Text summarization can be defined as reducing the dimensions of the expressions to be summarized without spoiling the meaning. Summarization can be performed as extractive and abstractive or using both together. In this study, we focus on abstractive summarization which can produce more human‐like summarization results. For the study we created a Turkish news summarization benchmark dataset from various news agency web portals by crawling the news title, short news, news content, and keywords for the last 5 years. The dataset is made publicly available for researchers. The deep learning network training was carried out by using the news headlines and short news contents from the prepared dataset and then the network was expected to create the news headline as the short news summary. To evaluate the performance of this study, Rouge‐1, Rouge‐2, and Rouge‐L were compared using precision, sensitivity and F1 measure scores. Performance values for the study were presented for each sentence as well as by averaging the results for 50 randomly selected sentences. The F1 Measure values are 0.4317, 0.2194, and 0.4334 for Rouge‐1, Rouge‐2, and Rouge‐L respectively. Performance results show that the approach is promising for Turkish text summarization studies and the prepared dataset will add value to the literature. [ABSTRACT FROM AUTHOR]

Summary: News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer‐generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer‐generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word‐image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out‐of‐domain BBC test dataset show that the proposed model outperforms the original pointer‐generator network, the generic image captioning method, the extractive news image captioning method, and the LDA‐based method according BLEU, METEOR, and ROUGL‐L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster‐based, and graph‐based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall‐oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n‐gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works. [ABSTRACT FROM AUTHOR]

With the launch of Chat GPT by open AI, a whole new world has opened up (and potentially a can of worms) in the scientific and publishing world. The potential role of Chat GPT and artificial intelligence (AI) in oral surgery and scientific writing can be significant, offering many potential benefits, but also raising important ethical considerations. Potential drawbacks and ethical considerations: Lack of human oversight: While AI can automate certain tasks, it cannot replace human expertise in scientific writing. [Extracted from the article]

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]

We propose a general framework for topic-specific summarization of large text corpora, and illustrate how it can be used for analysis in two quite different contexts: an Occupational Safety and Health Administration (OSHA) database of fatality and catastrophe reports (to facilitate surveillance for patterns in circumstances leading to injury or death), and legal decisions on workers' compensation claims (to explore relevant case law). Our summarization framework, built on sparse classification methods, is a compromise between simple word frequency-based methods currently in wide use, and more heavyweight, model-intensive methods such as latent Dirichlet allocation (LDA). For a particular topic of interest (e.g., mental health disability, or carbon monoxide exposure), we regress a labeling of documents onto the high-dimensional counts of all the other words and phrases in the documents. The resulting small set of phrases found as predictive are then harvested as the summary. Using a branch-and-bound approach, this method can incorporate phrases of arbitrary length, which allows for potentially rich summarization. We discuss how focus on the purpose of the summaries can inform choices of tuning parameters and model constraints. We evaluate this tool by comparing the computational time and summary statistics of the resulting word lists to three other methods in the literature. We also present a new R package, textreg. Overall, we argue that sparse methods have much to offer in text analysis and is a branch of research that should be considered further in this context. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016 [ABSTRACT FROM AUTHOR]

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

Economie crises are significant threats to macroeconomic stability. They can incur large costs and bring devastating effects on economies, with the effects often spilling over into other economies. Since 2007 we have witnessed the most severe and widely spread economic crisis since the Great Depression of the 1930s. In the meantime, a huge amount of ongoing media coverage, reporting, analysis and debate concerning the global economic crisis has been generated. In this study we explore the possibilities of applying text summarization tools to learn from text documents the various discussions surrounding the global economic crisis. Included in our analysis are blog posts and articles of highly influential economists, as well as official speeches and publications of government organizations. The ICSI-ILP extractive summarizer is applied in a large number of experiments, and the summary outputs are manually examined and evaluated. The results provide us with insights into the potential and limitations of state-of-the-art summarization systems when used to help us quickly learn and digest large amounts of textual information. The results also suggest different ways to break the limitations of text summarization technology. [ABSTRACT FROM AUTHOR]

Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures. [ABSTRACT FROM AUTHOR]