Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization

Maxime Peyrard, Judith Eckle-Kohler


Abstract
We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimization-based extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic Pyramid as the fitness function. Our experimental evaluation shows that our new framework significantly outperforms strong baselines regarding automatic Pyramid, and that there is much room for improvement in comparison with the upper-bound for automatic Pyramid.
Anthology ID:
P17-1100
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1084–1094
Language:
URL:
https://aclanthology.org/P17-1100
DOI:
10.18653/v1/P17-1100
Bibkey:
Cite (ACL):
Maxime Peyrard and Judith Eckle-Kohler. 2017. Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1084–1094, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization (Peyrard & Eckle-Kohler, ACL 2017)
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PDF:
https://aclanthology.org/P17-1100.pdf
Video:
 https://aclanthology.org/P17-1100.mp4