Call for participation:



FNS-2022 Shared Task: "Financial Narrative Summarisation"

To be held at The 4th Financial Narrative Processing Workshop (FNP 2022) and LREC 2022

Marseille, France 24 June 2022.

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Shared Task URL: http://wp.lancs.ac.uk/cfie/fns2022/

Workshop URL: http://wp.lancs.ac.uk/cfie/fnp2022/

Participation Form: http://bit.ly/34xWCCp

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Awards:



The winning team for FNS 2022 shared task will receive an achievement certificate. The team will also be given the chance to present their work at the workshop.



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Shared Task Description:



This year we are introducing two new languages in addition to English. The FNS shared task will include annual reports in Greek and Spanish as well as English.



The Structure-based Financial Narrative Summarisation (FNS) aims to demonstrate the value and challenges of applying automatic text summarisation to financial text written in English, usually referred to as financial narrative disclosures. The task dataset has been extracted from UK annual reports published in PDF file format. The participants will be asked to provide structured summaries, based on real-world, publicly available financial annual reports of UK firms by extracting information from different key sections. Participants will be asked to generate summaries that reflects the analysis and assessment of the financial trend of the business over the past year, as provided by annual reports. The evaluation of the summaries will be performed using Rouge automatic metrics.



The shared task requires extraction from different key sections found in the annual reports produced by UK firms listed on The London Stock Exchange (LSE), as well as Spanish and Greek annual reports. Those sections are usually referred to as "narrative sections" or "front-end" sections and they usually contain textual information and reviews by the firm's management and board of directors. Sections containing financial statements in terms of tables and numbers are usually referred to as "back-end" sections and are not supposed to be part of the narrative summaries. UK annual reports are lengthy documents with around 80 pages on average, some annual reports could span over more than 250 pages, making the summarisation task a challenging but an academically interesting one.



For the purpose of this task we will ask the participants to produce one summary for each annual report. The summary length should not exceed 1000 words. We advise that the summary is generated/extracted based on the narrative sections, therefore the participating summarisers need to be trained to detect narrative sections before creating the summaries. The MultiLing team along with help from Barcelona’s UPF summarisation team will help in organising the shared task including the generation of the evaluation results and final proceedings. The MultiLing team have a rich experience in organising summarisation tasks since 2011.





Participants should fill the short form to register to the task of interest. Registration will result in subscription to the task's mailing list. To register for multiple tasks, repeat the above registration process for each task of interest http://bit.ly/34xWCCp .



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Important dates:

1st Call for papers & shared task participants: 24 January 2022

2nd Call for papers & shared task participants: 20 February 2022

Training set release: 25 February 2022

Blind test set release: 25 March 2022

Systems submission: 1 April 2022

Release of results: 5 April 2022

Paper submission deadline: 8 April 2022

Papers notification of acceptance: 3 May 2022

Workshop date: 24 June 2022 (full day event)

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Contact:

For any questions on the shared task please contact us on:

fns.shared.task@gmail.com

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Shared Task Organisers:

-Mahmoud El-Haj (Lancaster University).

-Nadhem Zmandar (Lancaster University).

-Ahmed AbuRa’ed (University of British Columbia).

-Paul Rayson (Lancaster University).

-Nikiforos Pittaras (NCSR, Demokritos).

-Marina Litvak (Sami Shamoon College of Engineering).

-George Giannakopoulos (SKEL Lab – NCSR Demokritos)

-Antonio Moreno Sandoval (UAM, Madrid).