@inproceedings{foroutan-etal-2022-multilingual,
title = "Multilingual Text Summarization on Financial Documents",
author = "Foroutan, Negar and
Romanou, Angelika and
Massonnet, St{\'e}phane and
Lebret, R{\'e}mi and
Aberer, Karl",
editor = "El-Haj, Mahmoud and
Rayson, Paul and
Zmandar, Nadhem",
booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.fnp-1.7",
pages = "53--58",
abstract = "This paper proposes a multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summarization Task (FNS-2022). We developed two systems; the first uses a pre-trained abstractive summarization model that was fine-tuned on the downstream objective, the second approaches the problem as an extractive approach in which a similarity search is performed on the trained span representations. Both models aim to identify the beginning of the continuous narrative section of the document. The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document. The proposed systems achieve high performance in the given task, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.",
}
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<abstract>This paper proposes a multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summarization Task (FNS-2022). We developed two systems; the first uses a pre-trained abstractive summarization model that was fine-tuned on the downstream objective, the second approaches the problem as an extractive approach in which a similarity search is performed on the trained span representations. Both models aim to identify the beginning of the continuous narrative section of the document. The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document. The proposed systems achieve high performance in the given task, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.</abstract>
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%0 Conference Proceedings
%T Multilingual Text Summarization on Financial Documents
%A Foroutan, Negar
%A Romanou, Angelika
%A Massonnet, Stéphane
%A Lebret, Rémi
%A Aberer, Karl
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F foroutan-etal-2022-multilingual
%X This paper proposes a multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summarization Task (FNS-2022). We developed two systems; the first uses a pre-trained abstractive summarization model that was fine-tuned on the downstream objective, the second approaches the problem as an extractive approach in which a similarity search is performed on the trained span representations. Both models aim to identify the beginning of the continuous narrative section of the document. The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document. The proposed systems achieve high performance in the given task, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.
%U https://aclanthology.org/2022.fnp-1.7
%P 53-58
Markdown (Informal)
[Multilingual Text Summarization on Financial Documents](https://aclanthology.org/2022.fnp-1.7) (Foroutan et al., FNP 2022)
ACL
- Negar Foroutan, Angelika Romanou, Stéphane Massonnet, Rémi Lebret, and Karl Aberer. 2022. Multilingual Text Summarization on Financial Documents. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 53–58, Marseille, France. European Language Resources Association.