  With the explosive growth of textual information, summarization systems have
become increasingly important. This work aims to concisely indicate the current
state of the art in abstractive text summarization. As part of this, we outline
the current paradigm shifts towards pre-trained encoder-decoder models and
large autoregressive language models. Additionally, we delve further into the
challenges of evaluating summarization systems and the potential of
instruction-tuned models for zero-shot summarization. Finally, we provide a
brief overview of how summarization systems are currently being integrated into
commercial applications.
