UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization

Hoang-Quynh Le, Quoc-An Nguyen, Quoc-Hung Duong, Minh-Quang Nguyen, Huy-Son Nguyen, Tam Doan Thanh, Hai-Yen Thi Vuong, Trang M. Nguyen


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 automatically. Our approach achieves potential results, rank no.2 (evaluated on extractive references) and no.3 (evaluated on abstractive references) in the final evaluation.
Anthology ID:
2021.bionlp-1.38
Volume:
Proceedings of the 20th Workshop on Biomedical Language Processing
Month:
June
Year:
2021
Address:
Online
Venues:
BioNLP | NAACL
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
328–335
Language:
URL:
https://aclanthology.org/2021.bionlp-1.38
DOI:
10.18653/v1/2021.bionlp-1.38
Bibkey:
Cite (ACL):
Hoang-Quynh Le, Quoc-An Nguyen, Quoc-Hung Duong, Minh-Quang Nguyen, Huy-Son Nguyen, Tam Doan Thanh, Hai-Yen Thi Vuong, and Trang M. Nguyen. 2021. UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 328–335, Online. Association for Computational Linguistics.
Cite (Informal):
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization (Le et al., BioNLP 2021)
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PDF:
https://aclanthology.org/2021.bionlp-1.38.pdf