@inproceedings{xu-etal-2021-chichealth,
title = "{C}hic{H}ealth @ {MEDIQA} 2021: Exploring the limits of pre-trained seq2seq models for medical summarization",
author = "Xu, Liwen and
Zhang, Yan and
Hong, Lei and
Cai, Yi and
Sung, Szui",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.29",
doi = "10.18653/v1/2021.bionlp-1.29",
pages = "263--267",
abstract = "In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION). First, the candidate sentences are roughly estimated by using the Roberta model. Then the Markov chain model is used to evaluate the sentences in a fine-grained manner. Our team won the first place in overall performance, with the fourth place in MAS task, the seventh place in RRS task and the eleventh place in QS task. For the QS and RRS tasks, we investigate the performanceS of the end-to-end pre-trained seq2seq model. Experiments show that the methods of adversarial training and reverse translation are beneficial to improve the fine tuning performance.",
}
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<abstract>In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION). First, the candidate sentences are roughly estimated by using the Roberta model. Then the Markov chain model is used to evaluate the sentences in a fine-grained manner. Our team won the first place in overall performance, with the fourth place in MAS task, the seventh place in RRS task and the eleventh place in QS task. For the QS and RRS tasks, we investigate the performanceS of the end-to-end pre-trained seq2seq model. Experiments show that the methods of adversarial training and reverse translation are beneficial to improve the fine tuning performance.</abstract>
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%0 Conference Proceedings
%T ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization
%A Xu, Liwen
%A Zhang, Yan
%A Hong, Lei
%A Cai, Yi
%A Sung, Szui
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F xu-etal-2021-chichealth
%X In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION). First, the candidate sentences are roughly estimated by using the Roberta model. Then the Markov chain model is used to evaluate the sentences in a fine-grained manner. Our team won the first place in overall performance, with the fourth place in MAS task, the seventh place in RRS task and the eleventh place in QS task. For the QS and RRS tasks, we investigate the performanceS of the end-to-end pre-trained seq2seq model. Experiments show that the methods of adversarial training and reverse translation are beneficial to improve the fine tuning performance.
%R 10.18653/v1/2021.bionlp-1.29
%U https://aclanthology.org/2021.bionlp-1.29
%U https://doi.org/10.18653/v1/2021.bionlp-1.29
%P 263-267
Markdown (Informal)
[ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization](https://aclanthology.org/2021.bionlp-1.29) (Xu et al., BioNLP 2021)
ACL