@inproceedings{chen-etal-2023-ncuee,
title = "{NCUEE}-{NLP} at {B}io{L}ay{S}umm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the {PRIMERA} Models",
author = "Chen, Chao-Yi and
Yang, Jen-Hao and
Lee, Lung-Hao",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.62",
doi = "10.18653/v1/2023.bionlp-1.62",
pages = "586--591",
abstract = "This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2023-ncuee">
<titleInfo>
<title>NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chao-Yi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jen-Hao</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lung-Hao</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dina</namePart>
<namePart type="family">Demner-fushman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sophia</namePart>
<namePart type="family">Ananiadou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Cohen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance.</abstract>
<identifier type="citekey">chen-etal-2023-ncuee</identifier>
<identifier type="doi">10.18653/v1/2023.bionlp-1.62</identifier>
<location>
<url>https://aclanthology.org/2023.bionlp-1.62</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>586</start>
<end>591</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models
%A Chen, Chao-Yi
%A Yang, Jen-Hao
%A Lee, Lung-Hao
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chen-etal-2023-ncuee
%X This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance.
%R 10.18653/v1/2023.bionlp-1.62
%U https://aclanthology.org/2023.bionlp-1.62
%U https://doi.org/10.18653/v1/2023.bionlp-1.62
%P 586-591
Markdown (Informal)
[NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models](https://aclanthology.org/2023.bionlp-1.62) (Chen et al., BioNLP 2023)
ACL