@inproceedings{cho-etal-2019-multi,
title = "Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations",
author = "Cho, Sangwoo and
Li, Chen and
Yu, Dong and
Foroosh, Hassan and
Liu, Fei",
editor = "Wang, Lu and
Cheung, Jackie Chi Kit and
Carenini, Giuseppe and
Liu, Fei",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5412",
doi = "10.18653/v1/D19-5412",
pages = "98--103",
abstract = "Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion. Traditionally, both aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question. Whether, and to what extent, could contextualized sentence representations be used to improve the DPP framework? Our findings suggest that, despite the success of deep semantic representations, it remains necessary to combine them with surface indicators for effective identification of summary-worthy sentences.",
}
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<abstract>Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion. Traditionally, both aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question. Whether, and to what extent, could contextualized sentence representations be used to improve the DPP framework? Our findings suggest that, despite the success of deep semantic representations, it remains necessary to combine them with surface indicators for effective identification of summary-worthy sentences.</abstract>
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%0 Conference Proceedings
%T Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
%A Cho, Sangwoo
%A Li, Chen
%A Yu, Dong
%A Foroosh, Hassan
%A Liu, Fei
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the 2nd Workshop on New Frontiers in Summarization
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F cho-etal-2019-multi
%X Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion. Traditionally, both aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question. Whether, and to what extent, could contextualized sentence representations be used to improve the DPP framework? Our findings suggest that, despite the success of deep semantic representations, it remains necessary to combine them with surface indicators for effective identification of summary-worthy sentences.
%R 10.18653/v1/D19-5412
%U https://aclanthology.org/D19-5412
%U https://doi.org/10.18653/v1/D19-5412
%P 98-103
Markdown (Informal)
[Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations](https://aclanthology.org/D19-5412) (Cho et al., 2019)
ACL