Here we provide code for computing the upper-bound of PEAK and the optimizer that extracts summaries from a linear objective function.

If you reuse this software, please use the following citation:

@inproceedings{TUD-CS-2017-0073,
	title = {{Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization}},
	author = {Peyrard, Maxime and Eckle-Kohler, Judith},
	publisher = {Association for Computational Linguistics},
	volume = {Volume 1: Long Papers},
	booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)},
	pages = {(to appear)},
	month = aug,
	year = {2017},
	location = {Vancouver, Canada},
}

Abstract: We present a new framework for evaluating extractive summarizers, which is based on a principled representation as optimization problem. We prove that every extractive summarizer can be decomposed into an objective function and an optimization technique. We perform a comparative analysis and evaluation of several objective functions embedded in well-known summarizers regarding their correlation with human judgments. Our comparison of these correlations across two datasets yields surprising insights into the role and performance of objective functions in the different summarizers.

Contact person: Maxime Peyrard, [email protected]

• Python 2.7
• Numpy 1.11.1 (http://www.numpy.org)
• nltk 3.2.1 (http://www.nltk.org)
• clausIE

To test the installation just run: python pyrUpperBound.py