Look Up Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series'Musk' documentary gets first teaser trailer Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Mike Flanagan's'Carrie' gets its first trailer ahead of Oct. release'Shrek 5' teaser trailer: Shrek, Fiona, and Donkey are back to make you feel like an'All Star' In a science fiction novel, he would not be the hero. That's one of the lines of voice-over we hear in the first teaser for, filmmaker Alex Gibney's upcoming documentary about the founder of SpaceX and Tesla and the world's richest man. In the 48 second teaser we hear Elon Musk being described by different people over stock footage of his various endeavours -- in just a few short seconds he's referred to as both one of the greatest builders of all time and manifestly cruel and selfish, which gives some indication about where the documentary is headed.

Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series Netflix announces new'The Inbetweeners 3' movie with 40 second teaser Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Netflix's'Call My Agent!' movie trailer is formidable fun Ben Affleck and Kerry Washington fight to save their son in tense'Animals' teaser It's been over a decade since we last saw them on our screens, but are coming back. Netflix announced production of on Monday with a 40-second teaser, showing original cast-mates Simon Bird, Joe Thomas, James Buckley, and Blake Harrison climbing into their infamous yellow Fiat, which predictably refuses to start. Created by Iain Morris and Damon Beesley, ran for three season as a TV show from 2008 to 2010 and was followed by two movies.

Short videos are an effective tool for promoting contents and improving knowledge accessibility. While existing extractive video summarization methods struggle to produce a coherent narrative, existing abstractive methods cannot'quote' from the input videos, i.e., inserting short video clips in their outputs. In this work, we explore new video editing models for generating shorts that feature a coherent narrative with embedded video insertions extracted from a long input video. We propose a novel retrieval-embedded generation (REG) framework that allows a large language model to quote multimodal resources while maintaining a coherent narrative. Our proposed REGen system first generates the output story script with quote placeholders using a finetuned large language model, and then uses a multimodal retrieval model to replace the quote placeholders by selecting a video clip that best supports the narrative from a pool of candidate quotable video clips. We examine the proposed method on the task of documentary teaser generation, where short interview insertions are commonly used to support the narrative of a documentary. Our objective evaluations show that the proposed method can effectively insert short video clips while maintaining a coherent narrative. In a subjective survey, we show that our proposed method outperforms existing abstractive and extractive approaches in terms of coherence, alignment, and realism in documentary teaser generation.

Instant to reduce annoying "if you want" and teaser-style phrasing that users found intrusive. This change addresses widespread user complaints about persistent, clickbait-like follow-up prompts that negatively impacted the AI interaction experience. The update aims to create more natural, direct conversations by making ChatGPT less chatty and eliminating the bothersome response teasers. It wasn't all that long ago that ChatGPT was a constant nag, persistently dropping "Would you like me to?"-style questions at the end of its responses. OpenAI eventually tweaked the phrasing, dropping the question marks and going for "if you want"-style teasers that invited users to extend their chat sessions. Now, OpenAI has acknowledged that it went too far with the clickbaity follow-ups, noting in a recent update for one of its newest models that it's now cutting back on the teasers. "We're rolling out an update to GPT-5.3 Instant that improves follow-up tone and reduces teaser-style phrasing," reads a recent ChatGPT release note, which adds that users should soon see fewer follow-ups like "if you want," "you'll never believe," and "I can tell you three things that " Those teasers are, of course, a way for ChatGPT to keep subscribers chatting, but users have been complaining that the persistent follow-ups are more annoying than they are intriguing. "I hated it with a passion and hope it's completely gone," wrote one user on Reddit .

High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize full length articles. We show that this phenomenon is common across seven diverse languages and supports multi-document summarization. To scale data collection, we develop an automatic process, suited to varying linguistic resource levels. Finally, we apply this process to a Hebrew newspaper title, producing HEBTEASESUM, the first dedicated multi-document summarization dataset in Hebrew.

We revise the definition of clickbait, which lacks current consensus, and argue that the creation of a curiosity gap is the key concept that distinguishes clickbait from other related phenomena such as sensationalism and headlines that do not deliver what they promise or diverge from the article. Therefore, we propose a new definition: clickbait is a technique for generating headlines and teasers that deliberately omit part of the information with the goal of raising the readers' curiosity, capturing their attention and enticing them to click. We introduce a new approach to clickbait detection datasets creation, by refining the concept limits and annotations criteria, minimizing the subjectivity in the decision as much as possible. Following it, we created and release TA1C (for Te Ahorré Un Click, Spanish for Saved You A Click), the first open source dataset for clickbait detection in Spanish. It consists of 3,500 tweets coming from 18 well known media sources, manually annotated and reaching a 0.825 Fleiss' κ inter annotator agreement. We implement strong baselines that achieve 0.84 in F1-score.

Multiview point cloud registration is a fundamental task for constructing globally consistent 3D models. Existing approaches typically rely on feature extraction and data association across multiple point clouds; however, these processes are challenging to obtain global optimal solution in complex environments. In this paper, we introduce a novel correspondence-free multiview point cloud registration method. Specifically, we represent the global map as a depth map and leverage raw depth information to formulate a non-linear least squares optimisation that jointly estimates poses of point clouds and the global map. Unlike traditional feature-based bundle adjustment methods, which rely on explicit feature extraction and data association, our method bypasses these challenges by associating multi-frame point clouds with a global depth map through their corresponding poses. This data association is implicitly incorporated and dynamically refined during the optimisation process. Extensive evaluations on real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy, particularly in challenging environments where feature extraction and data association are difficult.

By any estimation, Minecraft is impossibly successful. The bestselling video game ever, as of last December it had 204 million monthly active players. Since it was first released in 2011, it has generated over 3bn ( 2.3bn) in revenue. What's more, its players have always been eager to demonstrate their fandom outside the boundaries of the game itself. In 2021, YouTube calculated that videos related to the game – tutorials, walk-throughs, homages, parodies – had collectively been viewed 1tn times. In short, it is a phenomenon.

Welcome to a new newsletter, with a bit of a new direction. While our mid-week edition tackles news specifics, this end-of-the-week missive combines the biggest news with more context, more things to read and watch, recommendations, easter eggs, inside baseball and stuff that interests our readers, alongside the breaking news, reviews and features you expect from Engadget. We'd love your feedback on what you'd like to see covered in these meatier editions -- hit me up at tma(at)engadget.com. Luckily for me, we kick things off with Samsung's big Unpacked event, launching three new phones and teasing two -- yes, two! -- more coming soon. Everything Samsung announced, including prices and launch dates (February 8 -- I'll save you a click), we collated here, but it was largely a fallow year for Galaxy S hardware, barring a substantially more powerful chip.

Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while necessitating maintaining audiovisual alignments, managing scene changes and preserving factual accuracy for the output teasers. Due to the lack of a publicly-available dataset, progress along this research direction has been hindered. In this work, we present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers, featuring multimodal data streams of video, speech, music, sound effects and narrations. With DocumentaryNet, we propose a new two-stage system for generating teasers from long documentaries. The proposed TeaserGen system first generates the teaser narration from the transcribed narration of the documentary using a pretrained large language model, and then selects the most relevant visual content to accompany the generated narration through language-vision models. For narration-video matching, we explore two approaches: a pretraining-based model using pretrained contrastive language-vision models and a deep sequential model that learns the mapping between the narrations and visuals. Our experimental results show that the pretraining-based approach is more effective at identifying relevant visual content than directly trained deep autoregressive models.