@inproceedings{liu-etal-2019-multi-lingual,
    title = "Multi-lingual {W}ikipedia Summarization and Title Generation On Low Resource Corpus",
    author = "Liu, Wei  and
      Li, Lei  and
      Huang, Zuying  and
      Liu, Yinan",
    editor = "Giannakopoulos, George",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-8904/",
    doi = "10.26615/978-954-452-058-8_004",
    pages = "17--25",
    abstract = "MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus. In this paper we proposed QDAS extractive summarization model enhanced by sentence2vec and try to apply transfer learning based on large multilingual pre-trained language model for Wikipedia Headline Generation task. We treat it as sequence labeling task and develop two schemes to handle with it. Experimental results have shown that large pre-trained model can effectively utilize learned knowledge to extract certain phrase using low resource supervised data."
}