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 differen-tially 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 summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.- Anthology ID:
- C16-1098
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1028–1038
- Language:
- URL:
- https://aclanthology.org/C16-1098
- DOI:
- Cite (ACL):
- Lei He, Wei Li, and Hai Zhuge. 2016. Exploring Differential Topic Models for Comparative Summarization of Scientific Papers. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1028–1038, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Exploring Differential Topic Models for Comparative Summarization of Scientific Papers (He et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1098.pdf