@inproceedings{qiu-etal-2023-identifying,
title = "Identifying {ESG} Impact with Key Information",
author = "Qiu, Le and
Peng, Bo and
Gu, Jinghang and
Hsu, Yu-Yin and
Chersoni, Emmanuele",
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi and
Sakaji, Hiroki and
Izumi, Kiyoshi",
booktitle = "Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing",
month = nov,
year = "2023",
address = "Bali, Indonesia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.finnlp-2.7",
doi = "10.18653/v1/2023.finnlp-2.7",
pages = "51--56",
abstract = "The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qiu-etal-2023-identifying">
<titleInfo>
<title>Identifying ESG Impact with Key Information</title>
</titleInfo>
<name type="personal">
<namePart type="given">Le</namePart>
<namePart type="family">Qiu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bo</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jinghang</namePart>
<namePart type="family">Gu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu-Yin</namePart>
<namePart type="family">Hsu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emmanuele</namePart>
<namePart type="family">Chersoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chung-Chi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hen-Hsen</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroya</namePart>
<namePart type="family">Takamura</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsin-Hsi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroki</namePart>
<namePart type="family">Sakaji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kiyoshi</namePart>
<namePart type="family">Izumi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bali, Indonesia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.</abstract>
<identifier type="citekey">qiu-etal-2023-identifying</identifier>
<identifier type="doi">10.18653/v1/2023.finnlp-2.7</identifier>
<location>
<url>https://aclanthology.org/2023.finnlp-2.7</url>
</location>
<part>
<date>2023-11</date>
<extent unit="page">
<start>51</start>
<end>56</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Identifying ESG Impact with Key Information
%A Qiu, Le
%A Peng, Bo
%A Gu, Jinghang
%A Hsu, Yu-Yin
%A Chersoni, Emmanuele
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%Y Sakaji, Hiroki
%Y Izumi, Kiyoshi
%S Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing
%D 2023
%8 November
%I Association for Computational Linguistics
%C Bali, Indonesia
%F qiu-etal-2023-identifying
%X The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.
%R 10.18653/v1/2023.finnlp-2.7
%U https://aclanthology.org/2023.finnlp-2.7
%U https://doi.org/10.18653/v1/2023.finnlp-2.7
%P 51-56
Markdown (Informal)
[Identifying ESG Impact with Key Information](https://aclanthology.org/2023.finnlp-2.7) (Qiu et al., FinNLP-WS 2023)
ACL
- Le Qiu, Bo Peng, Jinghang Gu, Yu-Yin Hsu, and Emmanuele Chersoni. 2023. Identifying ESG Impact with Key Information. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 51–56, Bali, Indonesia. Association for Computational Linguistics.