The Current State of Summarization
It provides a review for researchers and practitioners, but is incremental as it synthesizes existing knowledge without novel contributions.
This work outlines the current state of the art in abstractive text summarization, highlighting paradigm shifts towards pre-trained models and challenges in evaluation, without presenting new experimental results or specific numerical improvements.
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.