  In zero-shot multilingual extractive text summarization, a model is typically
trained on English summarization dataset and then applied on summarization
datasets of other languages. Given English gold summaries and documents,
sentence-level labels for extractive summarization are usually generated using
heuristics. However, these monolingual labels created on English datasets may
not be optimal on datasets of other languages, for that there is the syntactic
or semantic discrepancy between different languages. In this way, it is
possible to translate the English dataset to other languages and obtain
different sets of labels again using heuristics. To fully leverage the
information of these different sets of labels, we propose NLSSum (Neural Label
Search for Summarization), which jointly learns hierarchical weights for these
different sets of labels together with our summarization model. We conduct
multilingual zero-shot summarization experiments on MLSUM and WikiLingua
datasets, and we achieve state-of-the-art results using both human and
automatic evaluations across these two datasets.
