mlsanityData to 7 Oct 2026

The Current State of Summarization

arXiv 2305.04853 ↗First posted 8 May 2023cs.CL cs.AIcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 64 of its 97 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

64 of 97 references.

References · 64

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38 more
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50 most cited of 64 All 64 (JSON)

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024Graph-Based Retriever Captures the Long Tail of Biomedical Knowledgecited by 20
  2. 2024From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptionscited by 16
  3. 2024Zero-Shot Strategies for Length-Controllable Summarizationcited by 10
  4. 2025Summarizing Speech: A Comprehensive Surveycited by 5
  5. 2024Handling Numeric Expressions in Automatic Speech Recognitioncited by 1
  6. 2025An Automated Length-Aware Quality Metric for Summarizationcited by 1