Abstract
This paper describes the systems proposed by HULAT research group from Universidad Carlos III de Madrid (UC3M) and MeaningCloud (MC) company to solve the FNS 2020 Shared Task on summarizing financial reports. We present a narrative extractive approach that implements a statistical model comprised of different features that measure the relevance of the sentences using a combination of statistical and machine learning methods. The key to the model’s performance is its accurate representation of the text, since the word embeddings used by the model have been trained with the summaries of the training dataset and therefore capture the most salient information from the reports. The systems’ code can be found at https://github.com/jaimebaldeon/FNS-2020.- Anthology ID:
- 2020.fnp-1.19
- Volume:
- Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- FNP
- SIG:
- Publisher:
- COLING
- Note:
- Pages:
- 112–117
- Language:
- URL:
- https://aclanthology.org/2020.fnp-1.19
- DOI:
- Cite (ACL):
- Jaime Baldeon Suarez, Paloma Martínez, and Jose Luis Martínez. 2020. Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 112–117, Barcelona, Spain (Online). COLING.
- Cite (Informal):
- Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020 (Baldeon Suarez et al., FNP 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.fnp-1.19.pdf
- Code
- jaimebaldeon/fns-2020