Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Since most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy, different labeling algorithms have been proposed to extrapolate oracle extracts for model training. In this work, we identify two flaws with the widely used greedy labeling approach: it delivers suboptimal and deterministic oracles. To alleviate both issues, we propose a simple yet effective labeling algorithm that creates soft, expectation-based sentence labels. We define a new learning objective for extractive summarization which incorporates learning signals from multiple oracle summaries and prove it is equivalent to estimating the oracle expectation for each document sentence. Without any architectural modifications, the proposed labeling scheme achieves superior performance on a variety of summarization benchmarks across domains and languages, in both supervised and zero-shot settings.

Thirty-five years ago, New Zealand adopted a law declaring the official status of Te Reo Māori, the language spoken by the country's indigenous Māori people. Decades of repression put the language, also called simply te reo, under serious threat: only one in four Māori spoke it by 1960, with a very low percentage of speakers among children. Since then, the language has started regaining lost ground, enjoying formally equal status with English and being taught widely to New Zealand schoolchildren. Still, reviving it as a living language takes time and persistence. Lately, the nascent te reo renaissance is gaining added momentum with the help of artificial intelligence (AI).

In the back room of an old and graying building in the northernmost region of New Zealand, one of the most advanced computers for artificial intelligence is helping to redefine the technology's future. Te Hiku Media, a nonprofit Māori radio station run by life partners Peter-Lucas Jones and Keoni Mahelona, bought the machine at a 50% discount to train its own algorithms for natural-language processing. Mahelona, a native Hawaiian who settled in New Zealand after falling in love with the country, chuckles at the irony of the situation. "The computer is just sitting on a rack in Kaitaia, of all places--a derelict rural town with high poverty and a large Indigenous population. I guess we're a bit under the radar," he says. As a nonprofit journalism organization, we depend on your support to fund coverage of Indigenous issues and communities. Donate any amount today to become a Pulitzer Center Champion and receive exclusive benefits!

A chimpanzee has learned to walk again after an illness left him paralysed. The male, named Reo, was 24 when part of his spinal cord became inflamed, rendering him unable to move. Now, ten years later, thanks to a dedicated programme of training using a touch screen and reward training, he is able to walk again. Lead author Yoko Sakuraba of Kyoto University described the triumph in an article in Primates, the official journal of the Japan Monkey Centre. The study marks the first time a dedicated training progamme using touch screen technology to study chimpanzees' cognitive abilities has helped a chimp recover after paralysis.