Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
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Abstract (arXiv)
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 19 Mar 2022.
Builds on
16 of 28 references.
References · 16
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- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
- 2019Text Summarization with Pretrained Encoderscited by 553
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
- 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
- 2020Extractive Summarization as Text Matchingcited by 189
- 2019HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarizationcited by 120
- 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
- 2018Content Selection in Deep Learning Models of Summarizationcited by 64
- 2019Sentence Centrality Revisited for Unsupervised Summarizationcited by 64
4 more
- 2018Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarizationcited by 42
- 2019Searching for Effective Neural Extractive Summarization: What Works and What's Nextcited by 38
- 2021Is Everything in Order? A Simple Way to Order Sentencescited by 15
- 2016Improving Multi-Document Summarization via Text Classificationcited by 14
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 4; 2025: 3.
9 papers.
- 2024The Nature of NLP: Analyzing Contributions in NLP Paperscited by 12
- 2022Unsupervised Opinion Summarization Using Approximate Geodesicscited by 5
- 2022NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarizationcited by 5
- 2024Disentangling Specificity for Abstractive Multi-document Summarizationcited by 1
- 2024DEPTH: Discourse Education through Pre-Training Hierarchicallycited by 0
- 2024Rethinking Transformer-based Multi-document Summarization: An Empirical Investigationcited by 0
- 2025PreSumm: Predicting Summarization Performance Without Summarizingcited by 0
- 2025DiscoSum: Discourse-aware News Summarizationcited by 0
- 2025Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarizationcited by 0
- 2025Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization11 Sep 2025
- 2025DiscoSum: Discourse-aware News Summarization7 Jun 2025
- 2025PreSumm: Predicting Summarization Performance Without Summarizing7 Apr 2025
- 2024The Nature of NLP: Analyzing Contributions in NLP Papers29 Sep 2024
- 2024Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation16 Jul 2024
- 2024DEPTH: Discourse Education through Pre-Training Hierarchically13 May 2024
- 2024Disentangling Specificity for Abstractive Multi-document Summarization12 May 2024
- 2022NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization2 Dec 2022
- 2022Unsupervised Opinion Summarization Using Approximate Geodesics15 Sep 2022