Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
This addresses the issue of label mismatch across languages for researchers in multilingual NLP, though it is incremental as it builds on existing zero-shot summarization methods.
The paper tackles the problem of suboptimal monolingual labels in zero-shot multilingual extractive summarization by proposing NLSSum, which jointly learns hierarchical weights for different label sets and the summarization model, achieving state-of-the-art results on MLSUM and WikiLingua datasets.
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.