Summarizing Financial Reports with Positional Language Model

Natalia Vanetik; Elizaveta Podkaminer; Marina Litvak

doi:10.1109/BigData59044.2023.10386704

Summarizing Financial Reports with Positional Language Model

Natalia Vanetik, Elizaveta Podkaminer, Marina Litvak

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company's financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers
Pages	2877-2883
Number of pages	7
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386704
State	Published - 1 Jan 2023
Externally published	Yes
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

extractive summarization
financial summarization
multilingual
positional language model

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData59044.2023.10386704

Cite this

Vanetik, N., Podkaminer, E., & Litvak, M. (2023). Summarizing Financial Reports with Positional Language Model. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 2877-2883). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData59044.2023.10386704

Vanetik, Natalia ; Podkaminer, Elizaveta ; Litvak, Marina. / Summarizing Financial Reports with Positional Language Model. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers, 2023. pp. 2877-2883 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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title = "Summarizing Financial Reports with Positional Language Model",

abstract = "Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company's financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.",

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author = "Natalia Vanetik and Elizaveta Podkaminer and Marina Litvak",

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doi = "10.1109/BigData59044.2023.10386704",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

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editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

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Vanetik, N, Podkaminer, E & Litvak, M 2023, Summarizing Financial Reports with Positional Language Model. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers, pp. 2877-2883, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386704

Summarizing Financial Reports with Positional Language Model. / Vanetik, Natalia; Podkaminer, Elizaveta; Litvak, Marina.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers, 2023. p. 2877-2883 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Summarizing Financial Reports with Positional Language Model

AU - Vanetik, Natalia

AU - Podkaminer, Elizaveta

AU - Litvak, Marina

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company's financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.

AB - Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company's financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.

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BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

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A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

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A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - Institute of Electrical and Electronics Engineers

T2 - 2023 IEEE International Conference on Big Data, BigData 2023

Y2 - 15 December 2023 through 18 December 2023

ER -

Vanetik N, Podkaminer E, Litvak M. Summarizing Financial Reports with Positional Language Model. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers. 2023. p. 2877-2883. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386704

Summarizing Financial Reports with Positional Language Model

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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