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

Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
The most important obstacles facing multidocument 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
more » ... etworks. 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. 1
doi:10.18653/v1/p19-1098 dblp:conf/acl/ChoLFL19 fatcat:v4q2comewraivnqpytde3hmyze