Based on neural tag search, the multilingual abstracts of zero samples of Chinese Academy of Sciences and Microsoft Asiatic research were selected into ACL 2022

Abstract text summarization has achieved good performance in English , This is mainly due to the large-scale pre-training language model and abundant annotated corpus . But for other small languages , At present, it is difficult to obtain large-scale annotation data .

Institute of information engineering, Chinese Academy of Sciences and Microsoft Research Asia The joint proposal is based on Zero-Shot Multi language extraction text summarization model . The specific method is to use the extracted text summarization model pre trained in English to extract abstracts directly from other low resource languages ; And for multilingualism Zero-Shot Single language label deviation in , Put forward Multilingual tags （Multilingual Label） Annotation algorithm and Neural label search model （Neural Label Search for Summarization, NLSSum）.

Experimental results show that , Model NLSSum In a multilingual summary dataset MLSUM In all languages Baseline The score of the model . Among them, in Russian （Ru） On dataset , The performance of the zero sample model is close to that of the model obtained by using the full amount of supervised data .

The study was published in ACL 2022 On the long article of the main meeting .

Address of thesis ： https://aclanthology.org/2022.acl-long.42.pdf