Information extraction (IE) systems rely on structured data for training, but such annotated data is highly imbalanced across languages, with low-resource languages receiving little attention.Label projection techniques aim to bridge this gap by transferring structured annotations from high-resource to low-resource languages.However, existing methods are either inaccurate or too slow for large-scale use.This work aims to address this problem by developing a more effective method that remains sufficiently efficient for large-scale projection.In particular, we propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective, while controlling data influence during training.Experimental results across 50+ languages show that our framework consistently outperforms previous state-of-the-art methods while maintaining fast inference speed.In addition, we introduce EXP -the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.

Standard extractive systems suffer from the lack of gold training signals since existing corpora solely provide document and humanwritten summary pairs while disregarding extractive labels.As a result, existing methods resort to imperfect pseudo-labels that are both biased and error-prone, thereby hindering the learning process of extractive models.In contrast, text generators which are commonly employed in abstractive summarization can effortlessly overcome this predicament on account of flexible sequence-to-sequence architectures.Motivated to bypass this inherent limitation, we investigate the possibility of conducting extractive summarization with text generators.Through extensive experiments covering six summarization benchmarks, we show that highquality extractive summaries can be assembled via approximating the outputs (abstractive summaries) of these generators.Moreover, we find that the approximate summaries correlate positively with the auxiliary summaries (i.e. a better generator enables the production of better extractive summaries).Our results signify a new paradigm for training extractive summarizers i.e. learning with generation (abstractive) objectives rather than extractive schemes.