mlsanityData to 7 Oct 2026

Read Top News First: A Document Reordering Approach for Multi-Document News Summarization

arXiv 2203.10254 ↗First posted 19 Mar 2022cs.CLcited by 9

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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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  12. 2019Sentence Centrality Revisited for Unsupervised Summarizationcited by 64
4 more
  1. 2018Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarizationcited by 42
  2. 2019Searching for Effective Neural Extractive Summarization: What Works and What's Nextcited by 38
  3. 2021Is Everything in Order? A Simple Way to Order Sentencescited by 15
  4. 2016Improving Multi-Document Summarization via Text Classificationcited by 14

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Citing papers per year: 2022: 2; 2023: 0; 2024: 4; 2025: 3.

9 papers.