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2020 EMNLP EMNLP 2020

NMF Ensembles? Not for Text Summarization!

Abstract

AbstractNon-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.

❓ The Questioner
🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Alka Khurana , Vasudha Bhatnagar

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Core Methods > Matrix Factorization Natural Language Processing > Applications > Summarization

Keywords

ensemble learning topic modeling text summarization document representation document clustering ensemble method non-negative matrix factorization
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