  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.
