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
- Editors:
- Anna Rogers, João Sedoc, Anna Rumshisky
- Venue:
- insights
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 88–93
- Language:
- URL:
- https://aclanthology.org/2020.insights-1.14
- DOI:
- 10.18653/v1/2020.insights-1.14
- Cite (ACL):
- Alka Khurana and Vasudha Bhatnagar. 2020. NMF Ensembles? Not for Text Summarization!. In Proceedings of the First Workshop on Insights from Negative Results in NLP, pages 88–93, Online. Association for Computational Linguistics.
- Cite (Informal):
- NMF Ensembles? Not for Text Summarization! (Khurana & Bhatnagar, insights 2020)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2020.insights-1.14.pdf