# Multi-document Summarization via Deep Learning Techniques: A Survey

arXiv: https://arxiv.org/abs/2011.04843 · HTML: https://mlsanity.com/paper/2011.04843/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 30 of 58)

- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering](https://mlsanity.com/paper/2305.15387/) (2023)
- [Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation](https://mlsanity.com/paper/2305.01498/) (2023)
- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation](https://mlsanity.com/paper/2508.15658/) (2025)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers](https://mlsanity.com/paper/2308.13191/) (2023)
- [What's under the hood: Investigating Automatic Metrics on Meeting Summarization](https://mlsanity.com/paper/2404.11124/) (2024)
- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [Medical Question Summarization with Entity-driven Contrastive Learning](https://mlsanity.com/paper/2304.07437/) (2023)
- [Document Intelligence in the Era of Large Language Models: A Survey](https://mlsanity.com/paper/2510.13366/) (2025)
- [Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study](https://mlsanity.com/paper/2112.07536/) (2021)
- [Descartes: Generating Short Descriptions of Wikipedia Articles](https://mlsanity.com/paper/2205.10012/) (2022)
- [Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2303.06565/) (2023)
- [Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2306.04787/) (2023)
- [Multi-document Summarization: A Comparative Evaluation](https://mlsanity.com/paper/2309.04951/) (2023)
- [Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness](https://mlsanity.com/paper/2311.11211/) (2023)
- [Unsupervised Extraction of Dialogue Policies from Conversations](https://mlsanity.com/paper/2406.15214/) (2024)
- [GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?](https://mlsanity.com/paper/2407.10245/) (2024)
- [M2DS: Multilingual Dataset for Multi-document Summarisation](https://mlsanity.com/paper/2407.12336/) (2024)
- [TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs](https://mlsanity.com/paper/2407.19616/) (2024)
- [EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents](https://mlsanity.com/paper/2412.11814/) (2024)
- [PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization](https://mlsanity.com/paper/2203.01769/) (2022)
- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)
- [Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization](https://mlsanity.com/paper/2302.01148/) (2023)
- [Benchmarking LLMs on the Semantic Overlap Summarization Task](https://mlsanity.com/paper/2402.17008/) (2024)
- [Large Language Model-Based Generation of Discharge Summaries](https://mlsanity.com/paper/2512.06812/) (2025)

## Abstract (arXiv)

Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field.