NMF Ensembles? Not for Text Summarization!

Alka Khurana, Vasudha Bhatnagar


Abstract
Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.
Anthology ID:
2020.insights-1.14
Volume:
Proceedings of the First Workshop on Insights from Negative Results in NLP
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
88–93
Language:
URL:
https://www.aclweb.org/anthology/2020.insights-1.14
DOI:
10.18653/v1/2020.insights-1.14
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.insights-1.14.pdf
Optional supplementary material:
 2020.insights-1.14.OptionalSupplementaryMaterial.zip