The paper explores the relevance of the Text-To-Text Transfer Transformer
language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic
keyword extraction from short text passages. The evaluation is carried out on
the new Polish Open Science Metadata Corpus (POSMAC), which is released with
this paper: a collection of 216,214 abstracts of scientific publications
compiled in the CURLICAT project. We compare the results obtained by four
different methods, i.e. plT5kw, extremeText, TermoPL, KeyBERT and conclude that
the plT5kw model yields particularly promising results for both frequent and
sparsely represented keywords. Furthermore, a plT5kw keyword generation model
trained on the POSMAC also seems to produce highly useful results in
cross-domain text labelling scenarios. We discuss the performance of the model
on news stories and phone-based dialog transcripts which represent text genres
and domains extrinsic to the dataset of scientific abstracts. Finally, we also
attempt to characterize the challenges of evaluating a text-to-text model on
both intrinsic and extrinsic keyword extraction.

探讨了 Text-To-Text Transfer Transformer (T5) 语言模型在波兰语 (plT5) 中对短文本段进行内在和外在关键词提取的相关性。通过对新的波兰开放科学元数据语料库（POSMAC）的评估，比较了四种不同方法得到的结果，其中 plT5kw 模型在频繁和稀疏表示关键词方面得到了特别有前途的结果。还讨论了模型在新闻和电话对话稿件的表现，并尝试在内在和外部关键词提取的情况下评价文本模型的挑战。