@inproceedings{can-etal-2021-uetrice,
title = "{UET}rice at {MEDIQA} 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model",
author = "Can, Duy-Cat and
Nguyen, Quoc-An and
Duong, Quoc-Hung and
Nguyen, Minh-Quang and
Nguyen, Huy-Son and
Ngoc, Linh Nguyen Tran and
Ha, Quang-Thuy and
Tran, Mai-Vu",
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-1.36",
doi = "10.18653/v1/2021.bionlp-1.36",
pages = "311--319",
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 propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner up by ROUGE-2 F1 score (over 13 participated teams).",
}
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<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 propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner up by ROUGE-2 F1 score (over 13 participated teams).</abstract>
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%0 Conference Proceedings
%T UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model
%A Can, Duy-Cat
%A Nguyen, Quoc-An
%A Duong, Quoc-Hung
%A Nguyen, Minh-Quang
%A Nguyen, Huy-Son
%A Ngoc, Linh Nguyen Tran
%A Ha, Quang-Thuy
%A Tran, Mai-Vu
%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 can-etal-2021-uetrice
%X This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop. We propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner up by ROUGE-2 F1 score (over 13 participated teams).
%R 10.18653/v1/2021.bionlp-1.36
%U https://aclanthology.org/2021.bionlp-1.36
%U https://doi.org/10.18653/v1/2021.bionlp-1.36
%P 311-319
Markdown (Informal)
[UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model](https://aclanthology.org/2021.bionlp-1.36) (Can et al., BioNLP 2021)
ACL