The Current State of Summarization

TL;DR

This paper reviews the current advancements, challenges, and commercial applications of abstractive text summarization, emphasizing the shift towards pre-trained models and the potential of instruction-tuned systems.

Contribution

It provides a comprehensive overview of recent paradigm shifts, evaluation challenges, and the integration of summarization models into real-world applications.

Findings

Pre-trained encoder-decoder and autoregressive models dominate current summarization research.

Evaluation of summarization systems remains a significant challenge.

Instruction-tuned models show promise for zero-shot summarization.

Abstract

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.