Exploring differential topic models for comparative summarization of scientific papers

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Abstract

This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differentially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summarization methods significantly outperform the generic baselines and the state-of-the-art comparative summarization
methods under ROUGE metrics.

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Publication date11 Dec 2016
Publication titleProceedings of COLING 2016, the 26th International Conference on Computational Linguistics : technical papers
PublisherAssociation for Computational Linguistics
Pages1028-1038
Number of pages10
ISBN (Print)978-4-87974-702-0
Original languageEnglish
Event26th International Conference on Computational Linguistics - Osaka, Japan

Conference

Conference26th International Conference on Computational Linguistics
Abbreviated titleCOLING 2016
CountryJapan
CityOsaka
Period11/12/1617/02/17

Bibliographic note

-This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/

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