@inproceedings{zevallos-etal-2024-related-work,
title = "Related Work Is All You Need",
author = "Zevallos, Rodolfo Joel and
Ortega, John E. and
Irving, Benjamin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1210",
pages = "13874--13878",
abstract = "In modern times, generational artificial intelligence is used in several industries and by many people. One use case that can be considered important but somewhat redundant is the act of searching for related work and other references to cite. As an avenue to better ascertain the value of citations and their corresponding locations, we focus on the common {``}related work{''} section as a focus of experimentation with the overall objective to generate the section. In this article, we present a corpus with 400k annotations of that distinguish related work from the rest of the references. Additionally, we show that for the papers in our experiments, the related work section represents the paper just as good, and in many cases, better than the rest of the references. We show that this is the case for more than 74{\%} of the articles when using cosine similarity to measure the distance between two common graph neural network algorithms: Prone and Specter.",
}
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<abstract>In modern times, generational artificial intelligence is used in several industries and by many people. One use case that can be considered important but somewhat redundant is the act of searching for related work and other references to cite. As an avenue to better ascertain the value of citations and their corresponding locations, we focus on the common “related work” section as a focus of experimentation with the overall objective to generate the section. In this article, we present a corpus with 400k annotations of that distinguish related work from the rest of the references. Additionally, we show that for the papers in our experiments, the related work section represents the paper just as good, and in many cases, better than the rest of the references. We show that this is the case for more than 74% of the articles when using cosine similarity to measure the distance between two common graph neural network algorithms: Prone and Specter.</abstract>
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%0 Conference Proceedings
%T Related Work Is All You Need
%A Zevallos, Rodolfo Joel
%A Ortega, John E.
%A Irving, Benjamin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zevallos-etal-2024-related-work
%X In modern times, generational artificial intelligence is used in several industries and by many people. One use case that can be considered important but somewhat redundant is the act of searching for related work and other references to cite. As an avenue to better ascertain the value of citations and their corresponding locations, we focus on the common “related work” section as a focus of experimentation with the overall objective to generate the section. In this article, we present a corpus with 400k annotations of that distinguish related work from the rest of the references. Additionally, we show that for the papers in our experiments, the related work section represents the paper just as good, and in many cases, better than the rest of the references. We show that this is the case for more than 74% of the articles when using cosine similarity to measure the distance between two common graph neural network algorithms: Prone and Specter.
%U https://aclanthology.org/2024.lrec-main.1210
%P 13874-13878
Markdown (Informal)
[Related Work Is All You Need](https://aclanthology.org/2024.lrec-main.1210) (Zevallos et al., LREC-COLING 2024)
ACL
- Rodolfo Joel Zevallos, John E. Ortega, and Benjamin Irving. 2024. Related Work Is All You Need. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13874–13878, Torino, Italia. ELRA and ICCL.