Abstract
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 possibilities to scale up to larger input document collections by selecting a small number of sentences from each document prior to constructing the summary. Experiments were done on the DUC2004 dataset for multi-document summarization. We observe a higher performance over the original model, on par with more complex state-of-the-art methods.- Anthology ID:
- W17-4511
- Volume:
- Proceedings of the Workshop on New Frontiers in Summarization
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, Fei Liu
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–90
- Language:
- URL:
- https://aclanthology.org/W17-4511
- DOI:
- 10.18653/v1/W17-4511
- Cite (ACL):
- Demian Gholipour Ghalandari. 2017. Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 85–90, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization (Gholipour Ghalandari, 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W17-4511.pdf