  The foundation for the research of summarization in the Czech language was
laid by the work of Straka et al. (2018). They published the SumeCzech, a large
Czech news-based summarization dataset, and proposed several baseline
approaches. However, it is clear from the achieved results that there is a
large space for improvement. In our work, we focus on the impact of named
entities on the summarization of Czech news articles. First, we annotate
SumeCzech with named entities. We propose a new metric ROUGE_NE that measures
the overlap of named entities between the true and generated summaries, and we
show that it is still challenging for summarization systems to reach a high
score in it. We propose an extractive summarization approach Named Entity
Density that selects a sentence with the highest ratio between a number of
entities and the length of the sentence as the summary of the article. The
experiments show that the proposed approach reached results close to the solid
baseline in the domain of news articles selecting the first sentence. Moreover,
we demonstrate that the selected sentence reflects the style of reports
concisely identifying to whom, when, where, and what happened. We propose that
such a summary is beneficial in combination with the first sentence of an
article in voice applications presenting news articles. We propose two
abstractive summarization approaches based on Seq2Seq architecture. The first
approach uses the tokens of the article. The second approach has access to the
named entity annotations. The experiments show that both approaches exceed
state-of-the-art results previously reported by Straka et al. (2018), with the
latter achieving slightly better results on SumeCzech's out-of-domain testing
set.
