Abstract
Generating text from structured data is challenging because it requires bridging the gap between the data and natural language. In the generation of financial data-to-text, stock market summaries written by experts require long-term analysis of market prices, thus it is often not suitable to formulate the problem as an end-to-end generation task. In this work, we focus on generating input features that can be aligned for stock market summaries. In particular, we introduce a new corpus for the task and define a rule-based approach to automatically identify salient market features from market prices. We obtained baseline results using state-of-the-art pre-trained models. Experimental results show that these models can produce fluent text and fairly accurate descriptions. We end with a discussion of the limitations and challenges of the proposed task.
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We did not attempt to train on the large Longformer-LED model due insufficient GPU computing resources, as the large model cannot be fitted on a single 16Â GB GPU.
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Tan, A., Goh, HN., Wong, LK. (2021). Acquiring Input Features from Stock Market Summaries: A NLG Perspective. In: Chomphuwiset, P., Kim, J., Pawara, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2021. Lecture Notes in Computer Science(), vol 12832. Springer, Cham. https://doi.org/10.1007/978-3-030-80253-0_8
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