Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis


Abstract
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the objective function optimized by the greedy algorithm. This component builds on the graph-of-words representation of text and the k-core decomposition algorithm to assign meaningful scores to words. We evaluate our approach on the AMI and ICSI meeting speech corpora, and on the DUC2001 news corpus. We reach state-of-the-art performance on all datasets. Results indicate that our method is particularly well-suited to the meeting domain.
Anthology ID:
W17-4507
Volume:
Proceedings of the Workshop on New Frontiers in Summarization
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
48–58
Language:
URL:
https://aclanthology.org/W17-4507
DOI:
10.18653/v1/W17-4507
Bibkey:
Cite (ACL):
Antoine Tixier, Polykarpos Meladianos, and Michalis Vazirgiannis. 2017. Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 48–58, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization (Tixier et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4507.pdf
Code
 Tixierae/EMNLP2017_NewSum