Jaime Baldeon Suarez


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Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020
Jaime Baldeon Suarez | Paloma Martínez | Jose Luis Martínez
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

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.