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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Moreno-Sandoval A.AuthorPorta-Zamorano J.AuthorCarbajo-Coronado B.Author

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June 2, 2024
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Proceedings Paper
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The Financial Document Causality Detection Shared Task (FinCausal 2023)

Publicated to: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. 2855-2860 - 2023-01-01 (), DOI: 10.1109/BigData59044.2023.10386745

Authors:

Moreno-Sandoval A; Porta-Zamorano J; Carbajo-Coronado B; Samy D; Mariko D; El-Haj M
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Affiliations

Cairo University; Department of Spanish; Cairo; Egypt - Author
Lancaster University; UCREL; Lancaster; United Kingdom - Author
Universidad Autónoma de Madrid; Laboratorio de Lingüística Informática; Spain - Author
Yseop; Machine Learning Lab; Paris; France - Author
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Abstract

We introduce the FinCausal 2023 Shared Task on Causality Detection in Financial Documents and the corresponding FinCausal dataset. This paper also provides insights into the participating systems and their outcomes. The primary objective of this task is to identify whether an object, event or sequence of events can be considered the cause of a preceding event (the effect). This year, we presented two subtasks, one in English and another in Spanish. In both subtasks, participants were tasked with pinpointing, within causal sentences, the elements that pertained to the cause and those that related to the effect. We received system runs from five teams for the English subtask and three teams for the Spanish subtask. FinCausal 2023 is affiliated with the 5th Financial Narrative Processing Workshop (FNP 2023), hosted at IEEE BigData 2023 in Sorrento, Italy. © 2023 IEEE.
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Keywords

Causality detectionFinanceFinancial documentFinancial documentsFins (heat exchange)NlpParticipating systemsPrimary objectiveSequence of eventsSorrentoSubtask

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-05:

  • Scopus: 8
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 2.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 4 - Quality Education, with a probability of 43% according to the mBERT algorithm developed by Aurora University.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Egypt; France; United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MORENO SANDOVAL, ANTONIO) .

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Project objectives

La aportación persigue los siguientes objetivos: identificar si un objeto, evento o secuencia de eventos puede considerarse causa de un evento precedente; diseñar y presentar dos subtareas de detección de causalidad en documentos financieros, una en inglés y otra en español; evaluar la capacidad de los sistemas participantes para localizar, dentro de oraciones causales, los elementos correspondientes a la causa y al efecto; analizar los resultados obtenidos por los equipos que participaron en ambas subtareas; y proporcionar un conjunto de datos específico para la tarea, denominado FinCausal 2023, que sirva como referencia para futuras investigaciones en procesamiento de narrativas financieras.
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Most relevant results

El estudio presenta los resultados del FinCausal 2023 Shared Task sobre detección de causalidad en documentos financieros, destacando la participación y desempeño de los sistemas presentados. Se identificaron con éxito los elementos causales y efectos dentro de oraciones causales en dos subtareas, una en inglés y otra en español. Participaron cinco equipos en la subtarea en inglés y tres en la subtarea en español. El evento se vinculó con el 5th Financial Narrative Processing Workshop (FNP 2023), celebrado en IEEE BigData 2023 en Sorrento, Italia, en 2023. Estos resultados proporcionan una base sólida para futuros avances en el análisis automático de causalidad en textos financieros.
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