@inproceedings{SisLab4492,
       booktitle = {BioNLP-NAACL 2021},
           title = {UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization},
          author = {Hoang Quynh Le and Thi Hai Yen Vuong and Minh Trang Nguyen},
            year = {2021},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4492/},
        abstract = {This paper describes a system developed to
summarize multiple answers challenge in the
MEDIQA 2021 shared task collocated with
the BioNLP 2021 Workshop. We present
an abstractive summarization model based
on BART, a denoising auto-encoder for pre?training sequence-to-sequence models. As focusing on the summarization of answers to consumer health questions, we propose a query-driven filtering phase to choose useful information from the input document automat?ically. Our approach achieves potential results, rank no.2 (evaluated on extractive references) and no.3 (evaluated on abstractive references)
in the final evaluation.}
}