SumHiS: Extractive Summarization Exploiting Hidden Structure
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Abstract (arXiv)
Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden clustering structure of the text. Experimental results on CNN/DailyMail demonstrate that our approach generates more accurate summaries than both extractive and abstractive methods, achieving state-of-the-art results in terms of ROUGE-2 metric exceeding the previous approaches by 10%. Additionally, we show that hidden structure of the text could be interpreted as aspects.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 12 Jun 2024.
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References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
- 2019Text Summarization with Pretrained Encoderscited by 556
- 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 246
- 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 222
- 2020Extractive Summarization as Text Matchingcited by 189
- 2021SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarizationcited by 132
- 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
- 2019Discourse-Aware Neural Extractive Text Summarizationcited by 105
- 2020SparTerm: Learning Term-based Sparse Representation for Fast Text Retrievalcited by 45
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