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<p>To date, automatic summarization methods have been mostly developed for (and applied to) general news articles, whereas other document types have been neglected. In this paper, we introduce the task of summarizing financial earnings call transcripts, and we present a method for summarizing this text type essential for the financial industry. Earnings calls are briefing events common for public companies in many countries, typically in the form of conference calls held between company executives and analysts that consist of a spoken monologue part followed by moderated questions and answers.</p><p>We show that traditional methods work less well in this domain, we present a method suitable for summarizing earnings calls. Our large-scale evaluation on a new human-annotated corpus of summary-worthy sentences shows that this method outperforms a set of strong baselines, including a new one that we propose specifically for earnings calls. To the best of our knowledge, this is the first application of summarization to financial earnings calls transcripts, a primary source of information for financial professionals.</p>
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Nugent, Timothy; Leidner, Jochen L.; Gkotsis, George (2023): Extractive Summarization of Financial Earnings Call Transcripts. Advances in Information Retrieval: Proceedings of the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2-6, 2023 2, S. 3-15. DOI: 10.1007/978-3-031-28238-6_1
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The website of Coburg University of Applied Sciences was translated using translation software provided by a third-party provider such as DeepL. The official text is the German version of the website. No liability is assumed, either explicitly or implicitly, for the accuracy, reliability, or correctness of the translations into another language.

Extractive Summarization of Financial Earnings Call Transcripts

To date, automatic summarization methods have been mostly developed for (and applied to) general news articles, whereas other document types have been neglected. In this paper, we introduce the task of summarizing financial earnings call transcripts, and we present a method for summarizing this text type essential for the financial industry. Earnings calls are briefing events common for public companies in many countries, typically in the form of conference calls held between company executives and analysts that consist of a spoken monologue part followed by moderated questions and answers.

We show that traditional methods work less well in this domain, we present a method suitable for summarizing earnings calls. Our large-scale evaluation on a new human-annotated corpus of summary-worthy sentences shows that this method outperforms a set of strong baselines, including a new one that we propose specifically for earnings calls. To the best of our knowledge, this is the first application of summarization to financial earnings calls transcripts, a primary source of information for financial professionals.

Titel:

Extractive Summarization of Financial Earnings Call Transcripts

Veröffentlichungsdatum:

17.03.2023

Publikationsart:

Beitrag zu wissenschaftlicher Konferenz/Tagung

Forschungsschwerpunkt:

kein FSP zugeordnet

Medien:

Advances in Information Retrieval: Proceedings of the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2-6, 2023

DOI:

Weblink:

Heft:

Band:

2

Artikelnummer:

ISBN:

978-3-031-28237-9

Autoren:

Timothy Nugent, Jochen Leidner, George Gkotsis

Medien:

Advances in Information Retrieval: Proceedings of the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2-6, 2023

Herausgeber:

Springer

Seiten:

3-15

Open Access:

Peer reviewed:

Ja

Zitierung:

Nugent, Timothy; Leidner, Jochen L.; Gkotsis, George (2023): Extractive Summarization of Financial Earnings Call Transcripts. Advances in Information Retrieval: Proceedings of the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2-6, 2023 2, S. 3-15. DOI: 10.1007/978-3-031-28238-6_1

Autoren:

Timothy Nugent, Jochen Leidner, George Gkotsis