# Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization

arXiv: https://arxiv.org/abs/1708.07690 · HTML: https://mlsanity.com/paper/1708.07690/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2017.

## Built on by (most cited first, 7 of 7)

- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [Incremental Extractive Opinion Summarization Using Cover Trees](https://mlsanity.com/paper/2401.08047/) (2024)
- [CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization](https://mlsanity.com/paper/2105.14201/) (2021)
- [Supervising the Centroid Baseline for Extractive Multi-Document Summarization](https://mlsanity.com/paper/2311.17771/) (2023)
- [Distilling Opinions at Scale: Incremental Opinion Summarization using XL-OPSUMM](https://mlsanity.com/paper/2406.10886/) (2024)
- [Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning](https://mlsanity.com/paper/2211.07596/) (2022)
- [News Signals: An NLP Library for Text and Time Series](https://mlsanity.com/paper/2312.11399/) (2023)

## Abstract (arXiv)

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use a simple greedy algorithm to find the best summary. Furthermore, we show possi- bilities to scale up to larger input docu- ment collections by selecting a small num- ber of sentences from each document prior to constructing the summary. Experiments were done on the DUC2004 dataset for multi-document summarization. We ob- serve a higher performance over the orig- inal model, on par with more complex state-of-the-art methods.