Abstract
Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.- Anthology ID:
- D17-1020
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 210–220
- Language:
- URL:
- https://aclanthology.org/D17-1020
- DOI:
- 10.18653/v1/D17-1020
- Cite (ACL):
- Kexiang Wang, Tianyu Liu, Zhifang Sui, and Baobao Chang. 2017. Affinity-Preserving Random Walk for Multi-Document Summarization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 210–220, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Affinity-Preserving Random Walk for Multi-Document Summarization (Wang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/D17-1020.pdf