This week's Deep Learning Paper Recaps feature two significant research works, namely "Bridging the gap between streaming and non-streaming ASR systems by distilling ensembles of CTC and RNN-T models" and "BRIO: Bringing Order to Abstractive Summarization". The first paper focuses on improving streaming automatic speech recognition (ASR) models using knowledge from non-streaming models, resulting in a significant reduction in Word Error Rate for Spanish, Portuguese, and French. The second paper proposes a novel training method for abstractive summarization that involves assigning probability mass to candidates based on their quality, leading to new state-of-the-art results on several well-known datasets.