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GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization

arXiv 2304.03548 ↗First posted 7 Apr 2023cs.CLcited by 0

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Abstract (arXiv)

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at urlhttps://github.com/baoguangsheng/gemini.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 7 Apr 2023.

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References · 30

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2016Layer Normalizationcited by 5,669
  3. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  4. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
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  11. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
  12. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 294
18 more
  1. 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 246
  2. 2018WikiHow: A Large Scale Text Summarization Datasetcited by 204
  3. 2020Extractive Summarization as Text Matchingcited by 189
  4. 2018Bottom-Up Abstractive Summarizationcited by 177
  5. 2022BRIO: Bringing Order to Abstractive Summarizationcited by 161
  6. 2018Fast Abstractive Summarization with Reinforce-Selected Sentence Rewritingcited by 158
  7. 2021SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarizationcited by 132
  8. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  9. 2016Neural Summarization by Extracting Sentences and Wordscited by 116
  10. 2020What Have We Achieved on Text Summarization?cited by 65
  11. 2020Text Generation by Learning from Demonstrationscited by 64
  12. 2022SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarizationcited by 61
  13. 2017Text Summarization Techniques: A Brief Surveycited by 59
  14. 2021RefSum: Refactoring Neural Summarizationcited by 25
  15. 2019Summary Level Training of Sentence Rewriting for Abstractive Summarizationcited by 23
  16. 2021HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Modelscited by 12
  17. 2021Contextualized Rewriting for Text Summarizationcited by 8
  18. 2022A General Contextualized Rewriting Framework for Text Summarizationcited by 3

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