Acquiring Input Features from Stock Market Summaries: A NLG Perspective


Tan, Aristotle and Goh, Hui Ngo and Wong, Lai Kuan (2021) Acquiring Input Features from Stock Market Summaries: A NLG Perspective. In: International Conference on Multi-disciplinary Trends in Artificial Intelligence, 2-3 July 2021, Virtual Conference.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Natural language generation (Computer science)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 30 Aug 2021 06:57
Last Modified: 30 Aug 2021 06:57


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