@inproceedings{peyrard-eckle-kohler-2017-supervised,
title = "Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization",
author = "Peyrard, Maxime and
Eckle-Kohler, Judith",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1100",
doi = "10.18653/v1/P17-1100",
pages = "1084--1094",
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.",
}
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%0 Conference Proceedings
%T Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization
%A Peyrard, Maxime
%A Eckle-Kohler, Judith
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F peyrard-eckle-kohler-2017-supervised
%X 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.
%R 10.18653/v1/P17-1100
%U https://aclanthology.org/P17-1100
%U https://doi.org/10.18653/v1/P17-1100
%P 1084-1094
Markdown (Informal)
[Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization](https://aclanthology.org/P17-1100) (Peyrard & Eckle-Kohler, ACL 2017)
ACL