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- W4381586585 abstract "Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the most suitable summarization model for a given text. The proposed system employs a fully connected neural network that analyzes the input content and predicts which summarizer should score the best in terms of ROUGE score for a given input. The meta-model selects among four different summarization models, developed for the Slovene language, using different properties of the input, in particular its Doc2Vec document representation. The four Slovene summarization models deal with different challenges associated with text summarization in a less-resourced language. We evaluate the proposed SloMetaSum model performance automatically and parts of it manually. The results show that the system successfully automates the step of manually selecting the best model." @default.
- W4381586585 created "2023-06-22" @default.
- W4381586585 creator A5002299384 @default.
- W4381586585 creator A5020021079 @default.
- W4381586585 date "2023-06-20" @default.
- W4381586585 modified "2023-09-26" @default.
- W4381586585 title "One model to rule them all: ranking Slovene summarizers" @default.
- W4381586585 doi "https://doi.org/10.48550/arxiv.2306.11518" @default.
- W4381586585 hasPublicationYear "2023" @default.
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