# Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization

arXiv: https://arxiv.org/abs/2303.02677 · HTML: https://mlsanity.com/paper/2303.02677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization](https://mlsanity.com/paper/1808.06218/) (2018)
- [SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression](https://mlsanity.com/paper/2007.08954/) (2020)
- [DynE: Dynamic Ensemble Decoding for Multi-Document Summarization](https://mlsanity.com/paper/2006.08748/) (2020)

## Abstract (arXiv)

The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents and satisfies content diversity. This paper proposes a multi-document summarization approach based on hierarchical clustering of documents. It utilizes the constructed class tree of documents to extract both the sentences reflecting the commonality of all documents and the sentences reflecting the specificity of some subclasses of these documents for generating a summary, so as to satisfy the coverage and diversity requirements of multi-document summarization. Comparative experiments with different variant approaches on DUC'2002-2004 datasets prove the effectiveness of mining both the commonality and specificity of documents for multi-document summarization. Experiments on DUC'2004 and Multi-News datasets show that our approach achieves competitive performance compared to the state-of-the-art unsupervised and supervised approaches.