mlsanityData to 7 Oct 2026

The Extractive-Abstractive Axis: Measuring Content "Borrowing" in Generative Language Models

arXiv 2307.11779 ↗First posted 20 Jul 2023cs.CL cs.AI cs.CYcited by 1

Not on the map. Why?

Abstract (arXiv)

Generative language models produce highly abstractive outputs by design, in contrast to extractive responses in search engines. Given this characteristic of LLMs and the resulting implications for content Licensing & Attribution, we propose the the so-called Extractive-Abstractive axis for benchmarking generative models and highlight the need for developing corresponding metrics, datasets and annotation guidelines. We limit our discussion to the text modality.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

15 of 22 references.

References · 15

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  7. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
  8. 2023Evaluating Verifiability in Generative Search Enginescited by 297
  9. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
  10. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  11. 2020Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summarycited by 87
  12. 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 50
3 more
  1. 2022Data Governance in the Age of Large-Scale Data-Driven Language Technologycited by 32
  2. 2023Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluationcited by 24
  3. 2021Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarizationcited by 15

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisationcited by 0