End-to-end Training For Financial Report Summarization

Moreno La Quatra, Luca Cagliero


Abstract
Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets.
Anthology ID:
2020.fnp-1.20
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
Venue:
FNP
SIG:
Publisher:
COLING
Note:
Pages:
118–123
Language:
URL:
https://aclanthology.org/2020.fnp-1.20
DOI:
Bibkey:
Cite (ACL):
Moreno La Quatra and Luca Cagliero. 2020. End-to-end Training For Financial Report Summarization. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 118–123, Barcelona, Spain (Online). COLING.
Cite (Informal):
End-to-end Training For Financial Report Summarization (La Quatra & Cagliero, FNP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.fnp-1.20.pdf
Code
 morenolaquatra/sumto_financial_summarization