@inproceedings{yela-bello-etal-2021-multihumes,
title = "{M}ulti{H}um{ES}: Multilingual Humanitarian Dataset for Extractive Summarization",
author = "Yela-Bello, Jenny Paola and
Oglethorpe, Ewan and
Rekabsaz, Navid",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.146",
doi = "10.18653/v1/2021.eacl-main.146",
pages = "1713--1717",
abstract = "When responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making. While these documents contain valuable information, manually processing them is extremely time-consuming when an expedient response is necessary. To improve this process, effective summarization models are a valuable tool for humanitarian response experts as they provide digestible overviews of essential information in secondary data. This paper focuses on extractive summarization for the humanitarian response domain and describes and makes public a new multilingual data collection for this purpose. The collection {--} called MultiHumES{--} provides multilingual documents coupled with informative snippets that have been annotated by humanitarian analysts over the past four years. We report the performance results of a recent neural networks-based summarization model together with other baselines. We hope that the released data collection can further grow the research on multilingual extractive summarization in the humanitarian response domain.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yela-bello-etal-2021-multihumes">
<titleInfo>
<title>MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jenny</namePart>
<namePart type="given">Paola</namePart>
<namePart type="family">Yela-Bello</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ewan</namePart>
<namePart type="family">Oglethorpe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Navid</namePart>
<namePart type="family">Rekabsaz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-apr</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>When responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making. While these documents contain valuable information, manually processing them is extremely time-consuming when an expedient response is necessary. To improve this process, effective summarization models are a valuable tool for humanitarian response experts as they provide digestible overviews of essential information in secondary data. This paper focuses on extractive summarization for the humanitarian response domain and describes and makes public a new multilingual data collection for this purpose. The collection – called MultiHumES– provides multilingual documents coupled with informative snippets that have been annotated by humanitarian analysts over the past four years. We report the performance results of a recent neural networks-based summarization model together with other baselines. We hope that the released data collection can further grow the research on multilingual extractive summarization in the humanitarian response domain.</abstract>
<identifier type="citekey">yela-bello-etal-2021-multihumes</identifier>
<identifier type="doi">10.18653/v1/2021.eacl-main.146</identifier>
<location>
<url>https://aclanthology.org/2021.eacl-main.146</url>
</location>
<part>
<date>2021-apr</date>
<extent unit="page">
<start>1713</start>
<end>1717</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization
%A Yela-Bello, Jenny Paola
%A Oglethorpe, Ewan
%A Rekabsaz, Navid
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F yela-bello-etal-2021-multihumes
%X When responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making. While these documents contain valuable information, manually processing them is extremely time-consuming when an expedient response is necessary. To improve this process, effective summarization models are a valuable tool for humanitarian response experts as they provide digestible overviews of essential information in secondary data. This paper focuses on extractive summarization for the humanitarian response domain and describes and makes public a new multilingual data collection for this purpose. The collection – called MultiHumES– provides multilingual documents coupled with informative snippets that have been annotated by humanitarian analysts over the past four years. We report the performance results of a recent neural networks-based summarization model together with other baselines. We hope that the released data collection can further grow the research on multilingual extractive summarization in the humanitarian response domain.
%R 10.18653/v1/2021.eacl-main.146
%U https://aclanthology.org/2021.eacl-main.146
%U https://doi.org/10.18653/v1/2021.eacl-main.146
%P 1713-1717
Markdown (Informal)
[MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization](https://aclanthology.org/2021.eacl-main.146) (Yela-Bello et al., EACL 2021)
ACL