mlsanityData to 14 Oct 2026

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

arXiv 1910.11411 ↗First posted 24 Oct 2019cs.CLcited by 16

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Abstract (arXiv)

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion. Traditionally, these aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question of whether, and to what extent, contextualized representations can be used to improve DPP modeling. Our findings suggest that, despite the success of deep representations, it remains necessary to combine them with surface indicators for effective identification of summary sentences.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 5; 2024: 1; 2025: 2; 2026: 3.

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