Summarizing long-form narratives--such as books, movies, and TV scripts--requires capturing intricate plotlines, character interactions, and thematic coherence, a task that remains challenging for existing LLMs. We introduce NexusSum, a multi-agent LLM framework for narrative summarization that processes long-form text through a structured, sequential pipeline--without requiring fine-tuning. Our approach introduces two key innovations: (1) Dialogue-to-Description Transformation: A narrative-specific preprocessing method that standardizes character dialogue and descriptive text into a unified format, improving coherence. (2) Hierarchical Multi-LLM Summarization: A structured summarization pipeline that optimizes chunk processing and controls output length for accurate, high-quality summaries. Our method establishes a new state-of-the-art in narrative summarization, achieving up to a 30.0% improvement in BERTScore (F1) across books, movies, and TV scripts. These results demonstrate the effectiveness of multi-agent LLMs in handling long-form content, offering a scalable approach for structured summarization in diverse storytelling domains.

'Ask Dr. Drew' host Dr. Drew Pinsky breaks down key takeaways from the MAHA Commission's chronic disease report on'The Ingraham Angle.' As some health insurance companies have come under fire for allegedly using computer systems to shoot down claims, an Arizona law will soon make the practice illegal in the Grand Canyon State. Republican Arizona House Majority Whip Rep. Julie Willoughby sponsored the legislation, and it was recently signed into law by Democratic Gov. Katie Hobbs. House Bill 2175 requires a physician licensed in the state to conduct an "individual review" and use "independent medical judgment" to determine whether the claim should actually be denied. It also required a similar review of "a direct denial of a prior authorization of a service" that a provider asked for and "involves medical necessity."

Training transformer-based encoder-decoder models for long document summarization poses a significant challenge due to the quadratic memory consumption during training. Several approaches have been proposed to extend the input length at test time, but training with these approaches is still difficult, requiring truncation of input documents and causing a mismatch between training and test conditions. In this work, we propose CachED (Gradient $\textbf{Cach}$ing for $\textbf{E}$ncoder-$\textbf{D}$ecoder models), an approach that enables end-to-end training of existing transformer-based encoder-decoder models, using the entire document without truncation. Specifically, we apply non-overlapping sliding windows to input documents, followed by fusion in decoder. During backpropagation, the gradients are cached at the decoder and are passed through the encoder in chunks by re-computing the hidden vectors, similar to gradient checkpointing. In the experiments on long document summarization, we extend BART to CachED BART, processing more than 500K tokens during training and achieving superior performance without using any additional parameters.

Stephen Salter, who has died aged 85, was the inventor of the Salter's Duck, a wave-power device that was the first of its kind and promised to provide a new source of renewable energy for the world – until it was effectively killed off by the nuclear industry. In 1982, after eight years of development under Salter's direction at Edinburgh University, the United Kingdom Atomic Energy Authority (UKAEA) was asked by the government to see if the duck might be a cost-effective way of making large quantities of electricity. To the great surprise of Salter, and others, the UKAEA came to the conclusion that it was uneconomic, and that no further government funding should be given to the project. A decade later it emerged that thanks to a misplaced decimal point, the review had made Salter's duck look 10 times more expensive than the experiments showed it was likely to be. The UKAEA claimed this was just a mistake, but Salter, who had never been allowed to see the results of the secret evaluation, put it another way: asking the nuclear industry to evaluate an alternative source of energy was like putting King Herod in charge of a children's home, he suggested.

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.

File-transfer service WeTransfer BV opened its virtual space on May 1, almost seven weeks after closing its physical offices in New York, Los Angeles and Amsterdam as part of the global effort to slow the spread of the new coronavirus. Graphics reminiscent of early "Tomb Raider" videogames depict a version of the company's Dutch headquarters, adapted to include pool tables, techno music and in-jokes such as a "memorial" library named for the very- much-alive chief creative officer. Staff roam around in the form of avatars such as robots and panda bears. Gordon Willoughby, the chief executive of WeTransfer, said the platform helps provide the social experience of office life in the way that Zoom calls and Slack have replaced business meetings and desk-side chats. That is particularly valuable for recent hires, he said.