Unsupervised graph-clustering learning framework for financial news summarization

Wang, Jun; Tan, Jinghua; Jin, Hanlei; Qi, Shuo Feng

doi:10.1109/icdmw53433.2021.00094

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…We also removed articles labeled with categories related to the newspaper section rather than the news content, such as 'columns' and 'illustrated'. The resulting dataset has 96,819 news divided into power (22,022), market (20,970), sport (19,730), the world (17,130), and every day (16,967).…”

Section: Datasetsmentioning

confidence: 99%

“…This type of application can be utilized in a variety of contexts, such as news aggregation tools that organize news on related subjects like politics and sports. It can also be used to gather data, to be analyzed by other natural language processing techniques, to understand events such as natural disasters [11], epidemic monitoring [3,[12][13][14], or business events [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Breaking news: Unveiling a new dataset for Portuguese news classification and comparative analysis of approaches

Garcia,

Shiguihara,

Berton

2024

PLoS ONE

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Every day thousands of news are published on the web and filtering tools can be used to extract knowledge on specific topics. The categorization of news into a predefined set of topics is a subject widely studied in the literature, however, most works are restricted to documents in English. In this work, we make two contributions. First, we introduce a Portuguese news dataset collected from WikiNews an open-source media that provide news from different sources. Since there is a lack of datasets for Portuguese, and an existing one is from a single news channel, we aim to introduce a dataset from different news channels. The availability of comprehensive datasets plays a key role in advancing research. Second, we compare different architectures for Portuguese news classification, exploring different text representations (BoW, TF-IDF, Embedding) and classification techniques (SVM, CNN, DJINN, BERT) for documents in Portuguese, covering classical methods and current technologies. We show the trade-off between accuracy and training time for this application. We aim to show the capabilities of available algorithms and the challenges faced in the area.

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