In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization. We achieve an average ROUGE-2 score 0.163, generated summaries are coherent and look impressive at first glance. However, some of them contain misleading information that is not so easy to spot. We describe all the technical details and share our trained model and accompanying code in an online open-source repository, as well as some characteristic samples of the generated summaries.

翻译：在这项工作中,我们将第一种单一语言立陶宛变压器模型培训成立陶宛新闻文章数量相对较大,并将各种产出解码算法进行比较,以便进行抽象的新闻总结。我们平均达到ROUGE-2分0.163,制作的摘要是连贯的,乍一看令人印象深刻。然而,其中一些摘要包含误导信息,不容易发现。我们描述了所有技术细节,并在在线公开源码存储库中分享我们经过培训的模型和配套代码,以及生成的摘要的一些典型样本。