Topic Model Stability for Hierarchical Summarization

John Miller, Kathleen McCoy


Abstract
We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models. But we had to be sure that topic models were stable for the sampled corpora. To that end we developed a methodology for aligning multiple hierarchical structure topic models run over the same corpus under similar conditions, calculating a representative centroid model, and reporting stability of the centroid model. We ran stability experiments for standard corpora and a development corpus of Global Warming articles. We found flat and hierarchical structures of two levels plus the root offer stable centroid models, but hierarchical structures of three levels plus the root didn’t seem stable enough for use in hierarchical summarization.
Anthology ID:
W17-4509
Volume:
Proceedings of the Workshop on New Frontiers in Summarization
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–73
Language:
URL:
https://aclanthology.org/W17-4509
DOI:
10.18653/v1/W17-4509
Bibkey:
Cite (ACL):
John Miller and Kathleen McCoy. 2017. Topic Model Stability for Hierarchical Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 64–73, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Topic Model Stability for Hierarchical Summarization (Miller & McCoy, 2017)
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https://aclanthology.org/W17-4509.pdf
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