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Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
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
doi:10.18653/v1/p19-1098
dblp:conf/acl/ChoLFL19
fatcat:v4q2comewraivnqpytde3hmyze