# Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

arXiv: https://arxiv.org/abs/1910.11411 · HTML: https://mlsanity.com/paper/1910.11411/

## Key facts

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

## Built on by (most cited first, 16 of 16)

- [Toward Unifying Text Segmentation and Long Document Summarization](https://mlsanity.com/paper/2210.16422/) (2022)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [Diversity-Aware Batch Active Learning for Dependency Parsing](https://mlsanity.com/paper/2104.13936/) (2021)
- [Feasible Recourse Plan via Diverse Interpolation](https://mlsanity.com/paper/2302.11213/) (2023)
- [LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases](https://mlsanity.com/paper/2501.14114/) (2025)
- [Modeling Endorsement for Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2110.07844/) (2021)
- [Diverse Multi-Answer Retrieval with Determinantal Point Processes](https://mlsanity.com/paper/2211.16029/) (2022)
- [Unsupervised Multi-document Summarization with Holistic Inference](https://mlsanity.com/paper/2309.04087/) (2023)
- [Scaling DPPs for RAG: Density Meets Diversity](https://mlsanity.com/paper/2604.03240/) (2026)
- [Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents](https://mlsanity.com/paper/2609.05824/) (2026)
- [Supervising the Centroid Baseline for Extractive Multi-Document Summarization](https://mlsanity.com/paper/2311.17771/) (2023)
- [Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries](https://mlsanity.com/paper/2505.21859/) (2025)
- [FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing](https://mlsanity.com/paper/2609.38585/) (2026)
- [Efficient Failure Pattern Identification of Predictive Algorithms](https://mlsanity.com/paper/2306.00760/) (2023)
- [DPP-TTS: Diversifying prosodic features of speech via determinantal point processes](https://mlsanity.com/paper/2310.14663/) (2023)
- [Enhancing Jailbreak Attacks with Diversity Guidance](https://mlsanity.com/paper/2403.00292/) (2024)

## 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.