mlsanityData to 7 Oct 2026

AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature

arXiv 2302.06579 ↗First posted 13 Feb 2023cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Creating an abridged version of a text involves shortening it while maintaining its linguistic qualities. In this paper, we examine this task from an NLP perspective for the first time. We present a new resource, AbLit, which is derived from abridged versions of English literature books. The dataset captures passage-level alignments between the original and abridged texts. We characterize the linguistic relations of these alignments, and create automated models to predict these relations as well as to generate abridgements for new texts. Our findings establish abridgement as a challenging task, motivating future resources and research. The dataset is available at github.com/roemmele/AbLit.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

14 of 28 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  6. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  7. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  8. 2021Recursively Summarizing Books with Human Feedbackcited by 287
  9. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  10. 2019Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasetscited by 173
  11. 2020Exploring Content Selection in Summarization of Novel Chapterscited by 29
  12. 2021Document-Level Text Simplification: Dataset, Criteria and Baselinecited by 27
2 more
  1. 2020Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplificationcited by 12
  2. 2019The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summariescited by 8

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.