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The Web Intelligence - Multinational Enterprise (MNE) Groups Data Extraction Challenge

We are pleased to announce the outcome of the MNE Groups Data Extraction Challenge – the fourth round of European Statistics Awards on web intelligence.

In March 2025, Eurostat launched the MNE Groups Data Extraction with the primary goal of unveiling innovative methodologies and valuable data resources that could improve the production of European statistics.

We thank all participants for having contributed to the success of this round through their submissions.

The evaluation of the Web Intelligence Multinational Enterprise (MNE) Groups Data Extraction Challenge is now complete, and we are happy to announce the winners of the Accuracy, Reusability and Innovativeness Awards!

Congratulations to all the winners!

Web Intelligence – Multinational Enterprise Group Data Extraction Challenge aimed to develop approaches that automatically extract important annual financial data of MNE Groups.

The European Statistics Awards Programme Web Intelligence competitions aim at stimulating innovation when retrieving data from the world wide web for producing European statistics. The goal of the Multinational Enterprise Group Data Extraction Challenge was to develop approaches that automatically extract financial data from the World Wide Web for MNE Groups.

A multinational enterprise group, abbreviated as MNE Group and sometimes also called multinational corporation, or simply multinational or international corporation, is an enterprise producing goods or delivering services in more than one country. MNE Groups publish their financial data and annual financial results (financial statements) on documents, referred to hereby as "financial" reports.

The competition dataset contained 200 MNE groups. Participants were required to develop a method which extracts the requested annual financial data for each MNE Group along with the reference year automatically. One of the key elements of the competition was to develop robust approaches for generic use on identically structured arbitrarily chosen datasets.

The MNE Groups Data Extraction Challenge was launched in March 2025, with a final deadline for both submissions and documentation in June 2025.

A total of 57 teams, comprised of 70 individuals from 21 countries, signed up for this challenge, with 12 teams following through by submitting solutions for evaluation. The results of the evaluation are announced below.

The participants were competing for three types of awards:

  • Accuracy – aimed at reflecting the team’s performance in the extraction of annual financial data.
  • Reusability – aimed at rewarding submissions which show great potential to be scaled up to European statistics production. The Reusability award is intended to support the most thoroughly described and documented solutions, with the most scalable, well described and open approach.
  • Innovativeness – aimed at rewarding submissions which show the most originality in their approach. The Innovativeness award is intended to encourage cutting-edge solutions that go beyond current best methods.

Accuracy Award winners

Place Prize Team name Team members Country
1st place 10 000 EUR toad Thomas Faria France
2nd place 5 000 EUR MUR Miguel Ureña Spain
3rd place 3 000 EUR TheItalianJob Pasquale Maritato
Andrea Alessandrelli
Fabrizio Tomasso 		Italy

Reusability Award winners

Place Prize Team name Team members Country
1st place 10 000 EUR spacedace Máté Debreczeni
Martin Lényi 		Hungary
2nd place 5 000 EUR toad Thomas Faria France
3rd place 3 000 EUR MUR Miguel Ureña Spain

Innovativeness Award winners

Place Prize Team name Team members Country
1st place 5 000 EUR spacedace Máté Debreczeni
Martin Lényi 		Hungary
2nd place 3 000 EUR toad Thomas Faria France
3rd place 1 000 EUR MUR Miguel Ureña Spain

Since the Reusability and Innovativeness awards both required thoroughly described and documented solutions we are happy to share the solutions by all winners:

Solutions of the Reusability and Innovativeness Award winners