Because of the rich dynamical structure of videos andtheir ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal for visual representations. However, instantiating this idea, especially at large scale, has remained a significant artificial intelligence challenge. Here we present the Video Instance Embedding (VIE) framework, which trains deep nonlinear embeddings on video sequence inputs. By learning embedding dimensions that identify and group similar videos together, while pushing inherently different videos apart in the embedding space, VIE captures the strong statistical structure inherent in videos, without the need for external annotation labels. We find that, when trained on a large-scale video dataset, VIE yields powerful representations both for action recognition and single-frame object categorization, showing substantially improving on the state of the art wherever direct comparisons are possible. We show that a two-pathway model with both static and dynamic processingpathways is optimal, provide analyses indicating how the model works, and perform ablation studies showing the importance of key architecture and loss function choices. Our results suggest that deep neural embeddings are a promising approach to unsupervised video learning fora wide variety of task domains.

Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets.However, multidocument summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples.In this paper, we introduce Multi-News, the first large-scale MDS news dataset.Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets.We benchmark several methods on Multi-News and release our data and code in hope that this work will promote advances in summarization in the multidocument setting 1 .