@inproceedings{neves-etal-2019-evaluation,
title = "Evaluation of Scientific Elements for Text Similarity in Biomedical Publications",
author = "Neves, Mariana and
Butzke, Daniel and
Grune, Barbara",
editor = "Stein, Benno and
Wachsmuth, Henning",
booktitle = "Proceedings of the 6th Workshop on Argument Mining",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4515",
doi = "10.18653/v1/W19-4515",
pages = "124--135",
abstract = "Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text. We surveyed the literature for previously proposed schemes for rhetorical elements and present an overview of its current state of the art. We also searched for available tools using these schemes and applied four tools for our particular task of ranking biomedical abstracts based on text similarity. Comparison of the tools with two strong baselines shows that the predictions provided by the ArguminSci tool can support our use case of mining alternative methods for animal experiments.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="neves-etal-2019-evaluation">
<titleInfo>
<title>Evaluation of Scientific Elements for Text Similarity in Biomedical Publications</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mariana</namePart>
<namePart type="family">Neves</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Butzke</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="family">Grune</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 6th Workshop on Argument Mining</title>
</titleInfo>
<name type="personal">
<namePart type="given">Benno</namePart>
<namePart type="family">Stein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henning</namePart>
<namePart type="family">Wachsmuth</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Florence, Italy</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text. We surveyed the literature for previously proposed schemes for rhetorical elements and present an overview of its current state of the art. We also searched for available tools using these schemes and applied four tools for our particular task of ranking biomedical abstracts based on text similarity. Comparison of the tools with two strong baselines shows that the predictions provided by the ArguminSci tool can support our use case of mining alternative methods for animal experiments.</abstract>
<identifier type="citekey">neves-etal-2019-evaluation</identifier>
<identifier type="doi">10.18653/v1/W19-4515</identifier>
<location>
<url>https://aclanthology.org/W19-4515</url>
</location>
<part>
<date>2019-08</date>
<extent unit="page">
<start>124</start>
<end>135</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Evaluation of Scientific Elements for Text Similarity in Biomedical Publications
%A Neves, Mariana
%A Butzke, Daniel
%A Grune, Barbara
%Y Stein, Benno
%Y Wachsmuth, Henning
%S Proceedings of the 6th Workshop on Argument Mining
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F neves-etal-2019-evaluation
%X Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text. We surveyed the literature for previously proposed schemes for rhetorical elements and present an overview of its current state of the art. We also searched for available tools using these schemes and applied four tools for our particular task of ranking biomedical abstracts based on text similarity. Comparison of the tools with two strong baselines shows that the predictions provided by the ArguminSci tool can support our use case of mining alternative methods for animal experiments.
%R 10.18653/v1/W19-4515
%U https://aclanthology.org/W19-4515
%U https://doi.org/10.18653/v1/W19-4515
%P 124-135
Markdown (Informal)
[Evaluation of Scientific Elements for Text Similarity in Biomedical Publications](https://aclanthology.org/W19-4515) (Neves et al., ArgMining 2019)
ACL