Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu


Abstract
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks. The approach measures redundancy between a pair of sentences based on surface form and semantic information. We show that our DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets. Our findings are particularly meaningful for summarizing documents created by multiple authors containing redundant yet lexically diverse expressions.
Anthology ID:
P19-1098
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1027–1038
Language:
URL:
https://aclanthology.org/P19-1098
DOI:
10.18653/v1/P19-1098
Bibkey:
Cite (ACL):
Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, and Fei Liu. 2019. Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1027–1038, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (Cho et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1098.pdf
Video:
 https://vimeo.com/384475461
Code
 ucfnlp/summarization-dpp-capsnet
Data
SNLI