  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.
