Exploring differential topic models for comparative summarization of scientific papers

He, Lei, Li, Wei and Zhuge, Hai (2016). Exploring differential topic models for comparative summarization of scientific papers. IN: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. JPN: Association for Computational Linguistics.

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.

Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: -This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/
Event Title: 26th International Conference on Computational Linguistics
Event Type: Other
Event Dates: 2016-12-11 - 2017-02-17
Published Date: 2016-12-11
Authors: He, Lei
Li, Wei
Zhuge, Hai

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