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.
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Notes
- 1.
Within the scope of the RSDO project: https://www.cjvt.si/rsdo/.
- 2.
The demo is available at https://slovenscina.eu/en/povzemanje. The code repositories are available at https://github.com/azagsam/metamodel and https://github.com/clarinsi/SloSummarizer.
- 3.
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Acknowledgments
The work was partially supported by the Slovenian Research Agency (ARRS) core research programme P6-0411, as well as projects J6-2581, J7-3159, and CRP V5-2297.
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Žagar, A., Robnik-Šikonja, M. (2023). One Model to Rule Them All: Ranking Slovene Summarizers. In: Ekštein, K., Pártl, F., Konopík, M. (eds) Text, Speech, and Dialogue. TSD 2023. Lecture Notes in Computer Science(), vol 14102. Springer, Cham. https://doi.org/10.1007/978-3-031-40498-6_2
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