Ernst & Young (Israel)

In recent years, Document summarization has become an important area in Natural Language Processing (NLP) with the growth of long-rich texts, such as the Government Report (GovReport). Traditional transformer-based models achieve strong results on short texts, although they face challenges with very long documents owing to the loss of contextual information; thus, the segmentation methods and chain-of-thought prompting improved coverage. Hence, this research proposes an Attention-Guided Memory-Augmented Transformer (AGMGT) for long-document summarization with the help of the GovReport dataset, which contains government research reports and their expert-written summaries. The documents are then segmented and encoded using transformer encoders to manage the context length. Furthermore, a memory bank is constructed by storing salient information identified through attention scoring. Subsequently, cross-memory attention is applied for chunk-level micro-summarization to maintain consistency across sections. Then, the micro-summaries are aggregated into a global outline to generate a final summary using hierarchical attention over the outlines. Finally, the model is trained with cross-entropy and auxiliary coverage losses to generate concise and factually correct summaries of long government reports. The proposed AGMGT achieved better results in terms of ROUGE-1 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{0. 8 2 2 1}$</tex>), ROUGE-2 (0.6432), and ROUGE-L (0.7932) than the existing multilingual transformer 5 (mT5-Large) model.

Abstract.

Background: Mental health is a growing challenge, with women and girls disproportionately affected by conditions including anxiety and depression. These disparities are driven and exacerbated by factors such as socioeconomic status, race, and access to healthcare. Standards of care for these conditions vary, including combinations of in- and out-patient psychotherapy, pharmacotherapy, and behavioral therapies. Patients’ access to care is impacted by multifaceted system and individual level challenges, including provider shortages, cost, health literacy, and scheduling, with minority, economically disadvantaged, and neurodiverse women facing even greater barriers. Innovative digital health tools using artificial intelligence (AI) have the potential to address care gaps and narrow disparities for women and other vulnerable groups. Methods: A systematic review of available technologies was conducted to identify current AI solutions and digital health interventions focused on depression, anxiety, or overall mental wellbeing. A solution taxonomy was developed based on similar AI-enabled services. The solutions were categorized based on intervention modality, disease focus, target population, realized outcomes, and business model. The solution’s intent and ability to address mental health challenges for women was then assessed. Results: Current patient-facing mental health solutions include conversational AI support, healthy habit formation support, personalized care plans, screening tools, biometric risk factor identification, and digital music therapy. Among the examined technologies, 3 of 23 (13%) were designed for women and girls, and two of those solutions focused on women’s reproductive years. Just over half of assessed solutions (52%) use business-to-business-centric business models, requiring employers, payers, or providers to enable access. Conclusions: AI-driven approaches can complement traditional mental health services by providing targeted education, improved access, and personalized care. Despite the potential to reduce gender disparities in mental healthcare, most AI solutions do not specifically address the needs of women and girls. Opportunities for greater impact include tailoring interventions to better suit women from diverse backgrounds, improving access through business model innovation, and expanding clinical integration in alignment with evidence-based treatment. Future research should focus on the long-term impact of AI interventions on mental health outcomes, designing specific tools for women and girls, and the risk of exacerbating biases by using AI in mental healthcare.

Na entrevista, Gilmar de Carvalho aborda a mudança da técnica e da linguagem na publicidade nordestina. Tensiona política com a institucionalização das agências de publicidade regional. Detalha o nascimento do curso de Publicidade e Propaganda na Universidade Federal do Ceará, bem como narrativas, marcas e profissionais da publicidade cearense desde 1970. Crê que o técnico da publicística é desenhista de experiências que se figuram em modalidades da realidade.

Zuschüsse mit anschließender Tilgung von Gesellschafterverbindlichkeiten können als Rekapitalisierungsmaßnahme innerhalb eines Konzernverbunds in Betracht gezogen werden. Jedoch wirft die Vorgehensweise die Frage auf, ob dies nicht gegebenenfalls zu einem steuerwirksamen Forderungsverzicht führt, sodass der steuerlichen Würdigung eines Gesellschafterzuschusses wieder mehr Bedeutung zu schenken ist.