Liou Y.-TChen C.-CTang T.-HHuang H.-HHSIN-HSI CHEN2021-09-022021-09-022021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103057947&doi=10.1145%2f3437963.3441704&partnerID=40&md5=fa8301d865aa0265cd888ed8e5fb8fa3https://scholars.lib.ntu.edu.tw/handle/123456789/581356This paper demonstrates FinSense, a system that improves the working efficiency of financial information processing. Given the draft of a financial news story, FinSense extracts the explicit-mentioned stocks and further infers the implicit stocks, providing insightful information for decision making. We propose a novel graph convolutional network model that performs implicit financial instrument inference toward the in-domain data. In addition, FinSense generates candidate headlines for the draft, reducing a significant amount of time in journalism production. The proposed system also provides assistance to investors to sort out the information in the financial news articles. ? 2021 Owner/Author.Convolutional neural networks; Decision making; Finance; Information retrieval; Websites; Convolutional networks; Financial news; Working efficiency; Data miningconference paper10.1145/3437963.34417042-s2.0-85103057947