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This article details a collaboration between the Universidad Nacional de Córdoba in Argentina and IKUMI SRL, an educational technology company, to improve user experience with Mumuki, IKUMI SRL's open source platform for software programming training. These improvements utilized stochastic language models and automatic summarization to achieve the collaboration's two main objectives of improving learner-tutor interaction while using the platform.

The McKinsey Technology Trends Outlook 2024 provides an analysis of the top technology trends that will have a significant impact in the future. Despite challenging market conditions in 2023, investments in frontier technologies, such as generative AI (gen AI), show promise for future growth. The report highlights the synergy between robotics and gen AI, as well as advancements in AI capabilities. Digital trust and cybersecurity, as well as electrification and renewables, were notable trends in 2023. While technology equity investments declined, gen AI saw a significant increase in investments. The long-term outlook for technology trends remains positive, with continued innovation and interest in harnessing these technologies for future growth. The article also discusses various technology trends, their adoption rates in different industries, and the importance of aligning technology adoption strategies with the external ecosystem for successful integration. [Extracted from the article]

Researchers at Kobe University Graduate School of Medicine in Japan have conducted a study on the use of natural language processing with large language models for the automatic summarization of radiology reports. The study used two datasets, one comprising chest radiograph reports and the other comprising computed tomography and magnetic resonance imaging reports. Four Text-to-Text Transfer Transformer (T5) models were constructed and evaluated using quantitative metrics and manual evaluations by radiologists. The results showed that the T5 models were able to automatically summarize the radiology reports, with the majority of the summaries deemed clinically valuable by the radiologists. [Extracted from the article]

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A recent study conducted by researchers at the University of California Los Angeles (UCLA) explored the use of artificial intelligence (AI) models to summarize clinical reports and imaging notes in the field of cardiovascular research. The study evaluated the performance of various AI models, including GPT3davinci, BARTcnn, LongT5Booksum, LEDbooksum, LEDlegal, and LEDclinical. While the AI models showed promise in summarizing longitudinal aneurysm reports, none of them reached the level of quality required for clinical usage. The researchers suggest that further improvements and developments in NLP models may help streamline clinical workflow in the future. [Extracted from the article]

Video summarization addresses generating video summaries to help watchers grasp the content of a video without watching it entirely. Many methods have engaged in automatic video summarization. Although these methods have performed well, they still suffer from limited training data and sparse reward problems. We propose a Progressive Reinforcement Learning Video Summarization structure (PRLVS) with an unsupervised reward. The reward measures the information and quality the selected frames convey without annotations. Striving to earn higher rewards, our PRLVS adopts a "T"-type human thinking paradigm: choosing some key frames and checking if their adjacent frames are better than them. To simulate this paradigm, we decompose the flat strategy into a hierarchical strategy consisting of a horizontal policy and a vertical policy. These two policies are optimized alternatively, which densifies the reward while reducing the exploration space. Their cooperation also makes the agent capture the context information of the whole video at every step. Extensive experimental results on two benchmark databases (i.e., SumMe, TVSum) show that our PRLVS outperforms the comparisons and approaches the supervised methods, which indicates that it is significant to integrate our unsupervised reward into the progressive reinforcement learning structure to address limited annotation and sparse reward problems. • We design a progressive reinforcement learning structure for video summarization (PRLVS). • Our PRLVS simulates a "T"-type thinking paradigm by optimizing a horizontal and vertical policy alternatively. • Our PRLVS incorporates an unsupervised reward measuring the amount of information carried by the summaries. [ABSTRACT FROM AUTHOR]

For example, this is especially useful when the teleconference is held in a foreign language or a participant has to leave the teleconference for a short time. Moreover, most teleconference services allow recording the information that is exchanged during the teleconference and especially its speech, either in audio form or in text form (generated by the transcription performed on-line/off-line). [Extracted from the article]

Keywords for this news article include: Palakkad, India, Asia, Health and Medicine, Telemedicine, Indian Institute for Technology. Keywords: Palakkad; India; Asia; Health and Medicine; Telemedicine EN Palakkad India Asia Health and Medicine Telemedicine 994 994 1 04/03/23 20230404 NES 230404 2023 APR 9 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Investigators publish new report on Telemedicine. India, Asia, Health and Medicine, Telemedicine, Palakkad. [Extracted from the article]

Abstract: Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. [Copyright &y& Elsevier]

In this paper, we present a framework and a system that extracts events relevant to a query from a collection C of documents, and places such events along a timeline. Each event is represented by a sentence extracted from C, based on the assumption that "important" events are widely cited in many documents for a period of time within which these events are of interest. In our experiments, we used queries that are event types ("earthquake") and person names (e.g. "George Bush"). Evaluation was performed using G8 leader names as queries: comparison made by human evaluators between manually and system generated timelines showed that although manually generated timelines are on average more preferable, system generated timelines are sometimes judged to be better than manually constructed ones. [ABSTRACT FROM AUTHOR]

This article presents an abstract of the paper Automatic Summarization Focusing on Document Genre and Text Structure, by Yohei Seki. This dissertation proposes a new automatic summarization method focusing on document genre and text structure, and verifies its effectiveness. Text structure and document genre are used to extract important sentences from source documents and to generate output summaries. To date, automatic summarization research has focused principally on the topic of the document, using term frequency as a clue. The proposed method allows the user to search for specific types of information. Therefore, this proposed method produces summaries according to the type of information specified by the user as well as the topics of the documents. The three main aspects of the problem in the dissertation are as follows: extracting balanced contents of the source documents; summarization to discriminate between types of information that the user's desire to know and generating output summaries to improve the readability and reduce redundancy. As a preliminary stage of the research, single document summarization of single genre was investigated. Also in this research, document genre and text structure were used to summarize documents by discriminating between various types of information on the topics that the user wishes to identify. In this dissertation, the author's principal aim is to verify the effectiveness of document genre and text structure for automatic summarization.

The article focuses on the modification of a research about a novel approach for automatic summarization. It proposes the use of a document structure inside the automatic summarizer to address the failings of the integration of the CB-SEAS system and a method which identifies redundancy at its core. The newswire articles are categorized as chronologies, comparative news, enumerative news and classic news. The new approach employs an unsupervised clustering method to group related sentences and extract one sentence per cluster to build the summary.